Automotive Informatics and Communicative Systems: Principles in Vehicular Networks and Data Exchange Huaqun Guo Institute for Infocomm Research, A*STAR, Singapore
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Editorial Advisory Board Lawrence Wai-Choong Wong, National University of Singapore, Singapore Weihua Zhuang, University of Waterloo, Canada Ozan K. Tonguz, Carnegie Mellon University, USA Javier Ibañez-Guzmán, RENAULT S.A.S., France Todd Hubing, Clemson University, USA Nicholas F. Maxemchuk, Columbia University, USA Farid Naït-Abdesselam, University of Sciences and Technologies of Lille, France Yul Chu, University of Texas–Pan American, USA
List of Reviewers Sohel Anwar, Indiana University Purdue University Indianapolis, USA Raúl Aquino-Santos, Universidad de Colima, México Teck Yoong Chai, Institute for Infocomm Research, A*STAR, Singapore Yul Chu, University of Texas–Pan American, USA Gavin Holland, HRL Laboratories, LLC., USA Todd Hubing, Clemson University, USA Javier Ibañez-Guzmán, RENAULT S.A., France Tie Yan Li, Institute for Infocomm Research, A*STAR, Singapore Nicholas F. Maxemchuk, Columbia University, USA Farid Naït-Abdesselam, University of Sciences and Technologies of Lille, France Lek Heng Ngoh, Institute for Infocomm Research, A*STAR, Singapore Fabienne Nouvel, Laboratory IETR/INSA, France Stephan Olariu, Old Dominion University, USA Vasudha Ramnath, Institute for Infocomm Research, A*STAR, Singapore Biplab Sikdar, Rensselaer Polytechnic Institute, USA Joseph Chee Ming Teo, Institute for Infocomm Research, A*STAR, Singapore
Satish Ukkusuri, Rensselaer Polytechnic Institute, USA Ziyuan Wang, University of Melbourne, Australia Lawrence Wai-Choong Wong, National University of Singapore, Singapore Yew Fai Wong, National University of Singapore, Singapore Zonghua Zhang, National Institute of Information and Communication Technology, Japan Weihua Zhuang, University of Waterloo, Canada
Table of Contents
Foreword..............................................................................................................................................xiii Preface................................................................................................................................................... xv Acknowledgment................................................................................................................................. xxi Chapter I Introduction: An Emerging Area of Vehicular Networks and Data Exchange........................................ 1 Huaqun Guo, Institute for Infocomm Research, A*STAR, Singapore Chapter II Drive by Wire Systems: Impact on Vehicle Safety and Performance.................................................... 12 Sohel Anwar, Indiana University-Purdue University Indianapolis, USA Chapter III Electromagnetic Compatibility Issues in Automotive Communications............................................... 48 Todd H. Hubing, Clemson University International Center for Automotive Research, USA Chapter IV Automotive Network Architecture for ECUs Communications............................................................ 69 Fabienne Nouvel, Laboratory IETR-UMR, INSA, France Wilfried Gouret, Laboratory IETR-UMR, INSA, France Patrice Mazério, Laboratory IETR-UMR, INSA, France Ghais El Zein, Laboratory IETR-UMR, INSA, France Chapter V Enabling Secure Wireless Real-Time Vehicle Monitoring and Control................................................ 91 Lek Heng Ngoh, Institute for Infocomm Research, A*STAR, Singapore Chapter VI MAC and Routing Protocols for Vehicle to Vehicle Communications................................................ 105 Xiaobo Long, Rensselaer Polytechnic Institute, USA Biplab Sikdar, Rensselaer Polytechnic Institute, USA
Chapter VII Inter-Vehicular Communications Using Wireless Ad Hoc Networks.................................................. 120 Raúl Aquino-Santos, University of Colima, Mexico Víctor Rangel-Licea, National Autonomous University of Mexico, Mexico Miguel A. García-Ruiz, University of Colima, Mexico Apolinar González-Potes, University of Colima, Mexico Omar Álvarez-Cardenas, University of Colima, Mexico Arthur Edwards-Block, University of Colima, Mexico Margarita G. Mayoral-Baldivia, University of Colima, Mexico Sara Sandoval-Carrillo, University of Colima, Mexico Chapter VIII The Role of Communications in Cyber-Physical Vehicle Applications.............................................. 139 Nicholas F. Maxemchuk, Columbia University, USA & IMDEA Networks, Spain Patcharinee Tientrakool, Columbia University, USA Theodore L. Willke, Columbia University, USA & Intel Corporation, USA Chapter IX Integrating Traffic Flow Features to Characterize the Interference in Vehicular Ad Hoc Networks.............................................................................................................................................. 162 Lili Du, Purdue University, USA Satish Ukkusuri, Rensselaer Polytechnic Institute, USA Shivkumar Kalyanaraman, Rensselaer Polytechnic Institute, USA Chapter X Proactive Traffic Merging Strategies for Sensor-Enabled Cars........................................................... 180 Ziyuan Wang, University of Melbourne, Australia Lars Kulik, University of Melbourne, Australia Kotagiri Ramamohanarao, University of Melbourne, Australia Chapter XI The Localisation Problem in Cooperative Vehicle Applications......................................................... 200 Javier Ibañez-Guzmán, RENAULT S.A.S., France Chapter XII An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems............................................................................................................................. 219 Denis Gingras, Université de Sherbrooke, Canada Chapter XIII Efficient and Reliable Pseudonymous Authentication......................................................................... 247 Giorgio Calandriello, Politecnico di Torino, Italy Antonio Lioy, Politecnico di Torino, Italy
Chapter XIV Simulation of VANET Applications.................................................................................................... 264 Valentin Cristea, University Politehnica of Bucharest, Romania Victor Gradinescu, University Politehnica of Bucharest, Romania Cristian Gorgorin, University Politehnica of Bucharest, Romania Raluca Diaconescu, University Politehnica of Bucharest, Romania Liviu Iftode, Rutgers University, USA Chapter XV In-Vehicle Network Architecture for the Next-Generation Vehicles................................................... 283 Syed Masud Mahmud, Wayne State University, USA Compilation of References ............................................................................................................... 303 About the Contributors .................................................................................................................... 330 Index.................................................................................................................................................... 338
Detailed Table of Contents
Foreword..............................................................................................................................................xiii Preface................................................................................................................................................... xv Acknowledgment................................................................................................................................. xxi Chapter I Introduction: An Emerging Area of Vehicular Networks and Data Exchange........................................ 1 Huaqun Guo, Institute for Infocomm Research, A*STAR, Singapore This chapter gives an overview of this emerging area of vehicular networks, its potential applications, its potential wireless technologies for data exchange, and its research activities in the Europe, the United States (U.S.), Japan, and Singapore. Chapter II Drive by Wire Systems: Impact on Vehicle Safety and Performance.................................................... 12 Sohel Anwar, Indiana University-Purdue University Indianapolis, USA An overview of the drive-by-wire technology is presented along with in-depth coverage of salient drive by systems such as throttle-by-wire, brake-by-wire, and steer-by-wire systems, and hybrid-electric propulsion. A review of drive-by-wire system benefits in performance enhancements and vehicle active safety is then discussed. This is followed by in-depth coverage of technological challenges that must be overcome before drive-by-wire systems can be production ready. Current state of the art of possible solutions to these technological hurdles is then discussed. Future trends in the drive-by-wire systems and economic and commercialization aspects of these system are presented at the conclusion of the chapter. Chapter III Electromagnetic Compatibility Issues in Automotive Communications............................................... 48 Todd H. Hubing, Clemson University International Center for Automotive Research, USA
This chapter reviews automotive EMC requirements and discusses the design of automotive electronics for EMC. The objective of the chapter is to provide non-EMC engineers and engineering managers with basic information that will help them recognize the importance of designing for electromagnetic compatibility, rather than addressing electronic noise problems as they arise. Chapter IV Automotive Network Architecture for ECUs Communications............................................................ 69 Fabienne Nouvel, Laboratory IETR-UMR, INSA, France Wilfried Gouret, Laboratory IETR-UMR, INSA, France Patrice Mazério, Laboratory IETR-UMR, INSA, France Ghais El Zein, Laboratory IETR-UMR, INSA, France This chapter introduces the most widely used automotive networks like LIN (Local Interconnect Network), CAN (Controller Area Network), MOST (Media-Oriented Systems Transport), and FlexRay. To fulfill the increasing demand of intra-vehicle communications, a new technique based on power line communication (PLC) is then proposed. This allows the transmission of both power and messages without functional barriers. On the other hand, there are several infotainment applications (like mobile phones, laptop computers) pushing for the adoption of intra-vehicle wireless communications. Thus, some potential wireless technologies used in the automotive domain, namely Bluetooth, IEEE 802.11 b/g wireless technology – WiFi, and Zigbee are covered here. Finally, the chapter highlights the challenges of these wired or wireless alternative solutions in automotive networks. Chapter V Enabling Secure Wireless Real-Time Vehicle Monitoring and Control................................................ 91 Lek Heng Ngoh, Institute for Infocomm Research, A*STAR, Singapore In this chapter, the author extends the use of these embedded vehicular networks by proposing to remotely monitor and control the vehicles through them, in order to realize safety and driver assistance related applications. To accomplish this task, additional technologies such as real-time wireless communications and data security are required, and each of them is introduced and described in this chapter. Chapter VI MAC and Routing Protocols for Vehicle to Vehicle Communications................................................ 105 Xiaobo Long, Rensselaer Polytechnic Institute, USA Biplab Sikdar, Rensselaer Polytechnic Institute, USA Numerous efforts are currently under progress to enhance the safety and efficiency of vehicular traffic through intelligent transportation systems. In addition, the growing demand for access to data and information from human users on the go has created the need for advanced vehicle-to-vehicle and vehicleto-roadside communication systems capable of high data rates and amenable to high degrees of node mobility. Vehicular communications and networks are expected to be used for a number of purposes such as for enabling mobile users to transfer data and information from other networks such as the Internet and also for implementing services such as Intersection Decision Systems (IDS), Automated Highway Systems (AHS), and Advanced Vehicle Safety Systems (AVS). In this chapter the authors describe me-
dium access control (MAC) and routing protocols for vehicular networks and the various factors that affect their design and performance. Chapter VII Inter-Vehicular Communications Using Wireless Ad Hoc Networks.................................................. 120 Raúl Aquino-Santos, University of Colima, Mexico Víctor Rangel-Licea, National Autonomous University of Mexico, Mexico Miguel A. García-Ruiz, University of Colima, Mexico Apolinar González-Potes, University of Colima, Mexico Omar Álvarez-Cardenas, University of Colima, Mexico Arthur Edwards-Block, University of Colima, Mexico Margarita G. Mayoral-Baldivia, University of Colima, Mexico Sara Sandoval-Carrillo, University of Colima, Mexico This chapter proposes a new routing algorithm that allows communication in vehicular ad hoc networks. In vehicular ad hoc networks, the transmitter node cannot determine the immediate future position of the receiving node beforehand. Furthermore, rapid topological changes and limited bandwidth compound the difficulties nodes experience when attempting to exchange position information. Chapter VIII The Role of Communications in Cyber-Physical Vehicle Applications.............................................. 139 Nicholas F. Maxemchuk, Columbia University, USA & IMDEA Networks, Spain Patcharinee Tientrakool, Columbia University, USA Theodore L. Willke, Columbia University, USA & Intel Corporation, USA The authors describe applications that improve the operation of automobiles, control traffic lights, and distribute the load on roadways. The requirements on the communications protocols that implement the applications are determined and a new communications paradigm, neighborcast, is described. Neighborcast communicates between nearby entities, and is particularly well suited to transportation applications. Chapter IX Integrating Traffic Flow Features to Characterize the Interference in Vehicular Ad Hoc Networks.............................................................................................................................................. 162 Lili Du, Purdue University, USA Satish Ukkusuri, Rensselaer Polytechnic Institute, USA Shivkumar Kalyanaraman, Rensselaer Polytechnic Institute, USA The research in this chapter investigates several fundamental issues, such as the connectivity, the reachability, the interference, and the capacity, with respect to information propagation in VANETs. The authors’ work is distinguished with previous efforts, since they incorporate the characteristics of traffic into these issues in the communication layer of VANETs; this mainly address the issue of the interference. Previous efforts to solve this problem only consider static network topologies. However, high node mobility and dynamic traffic features make the interference problem in VANETs quite different.
Chapter X Proactive Traffic Merging Strategies for Sensor-Enabled Cars........................................................... 180 Ziyuan Wang, University of Melbourne, Australia Lars Kulik, University of Melbourne, Australia Kotagiri Ramamohanarao, University of Melbourne, Australia This chapter surveys traffic control strategies for optimizing traffic flow on highways, with a focus on more adaptive and flexible strategies facilitated by current advancements in sensor-enabled cars and vehicular ad hoc networks (VANETs). The authors investigate proactive merging strategies assuming that sensor-enabled cars can detect the distance to neighboring cars and communicate their velocity and acceleration among each other. Proactive merging strategies can significantly improve traffic flow by increasing it up to 100% and reduce the overall travel delay by 30%. Chapter XI The Localisation Problem in Cooperative Vehicle Applications......................................................... 200 Javier Ibañez-Guzmán, RENAULT S.A.S., France In this chapter, V2V and V2I applications are considered as a spatio-temporal problem. The tenet is that sharing information can be made only if this is time stamped and related to a spatial description of the information sources. The chapter formulates the spatio-temporal problem having as constraint the precision of the pose estimates of the vehicles involved. It regards the localisation problem and accuracy of digital road maps as a combined issue that needs to be addressed for the successful deployment of cooperative vehicle applications. Two case studies, intersection safely and an overtaking manoeuvre are included. Recommendations on the precision limits of the vehicle pose estimations and the potential uncertainties that need to be considered when designing V2V and V2I applications complete the chapter. Chapter XII An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems............................................................................................................................. 219 Denis Gingras, Université de Sherbrooke, Canada In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. Chapter XIII Efficient and Reliable Pseudonymous Authentication......................................................................... 247 Giorgio Calandriello, Politecnico di Torino, Italy Antonio Lioy, Politecnico di Torino, Italy Privacy, security, and reliability are key requirements in deploying vehicular ad-hoc networks (VANET). Without those the VANET technology will not be suitable for market diffusion. In this chapter, the au-
thors are concerned with how to fulfill these requirements by using pseudonym-based authentication, designing security schemes that do not endanger transport safety while maintaining low overhead. At the same time the design improves the system usability by allowing nodes to self-generate their own pseudonyms. Chapter XIV Simulation of VANET Applications.................................................................................................... 264 Valentin Cristea, University Politehnica of Bucharest, Romania Victor Gradinescu, University Politehnica of Bucharest, Romania Cristian Gorgorin, University Politehnica of Bucharest, Romania Raluca Diaconescu, University Politehnica of Bucharest, Romania Liviu Iftode, Rutgers University, USA This chapter systematically presents actual issues regarding the simulation of VANET applications. Some of them refer to challenges in developing VANET simulators. The chapter discusses simulator architectures, models used for representing the communication among vehicles, vehicles mobility features, and simulation tool implementation methods. A critical analysis of the solutions adopted in some well-known actual simulators is also included. Chapter XV In-Vehicle Network Architecture for the Next-Generation Vehicles................................................... 283 Syed Masud Mahmud, Wayne State University, USA This book chapter describes a number of ways using which the networks of future vehicles could be designed and implemented in a cost-effective manner. The book chapter also shows how simulation models can be developed to evaluate the performance of various types of in-vehicle network topologies and select the most appropriate topology for given requirements and specifications. Compilation of References ............................................................................................................... 303 About the Contributors .................................................................................................................... 330 Index.................................................................................................................................................... 338
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Foreword
Huaqun Guo has introduced the emerging areas of vehicular networks in the forms of Intra-Vehicle, Vehicleto-Vehicle, and Vehicle-to-Infrastructure communications and edited this new book to reflect the advance information technologies that shape the modern automobiles. These new technologies on automotive informatics and communicative systems will enable a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment to be incorporated into modern automobile designs. Over the last century, the design, manufacture and operation of the automobile have grown into complex system integration paradigms cutting across applications of traditional disciplines in physical sciences, engineering, social and behavioral sciences and business. Today, this complexity is compounded and accelerated by the advent of enabling technologies in advanced materials, sensing, actuation, computing, controls, diagnostics, electronics and software, all amid myriad – and often conflicting – policy changes. This creates new possibilities and challenges in simultaneously providing effective means of transportation - with a high degree of driver and occupant safety - along with reduced energy use and environmental impact. Informatics, telematics, electronics and communication systems play an ever increasing role in the advancement of the automobile and are critical from a number of perspectives. The advances that are most easily noticed by a consumer are vehicle options such as infotainment systems, navigation systems, and connectivity such as Bluetooth. However, other onboard systems such as active stability control, engine control, and the several supporting in-vehicle communication networks and protocols are the real technologies that are propelling the automobile into the 21st Century. Such systems are key elements in achieving the desired operational characteristics of the vehicle such as performance, emissions, safety and fuel efficiency. To achieve these ever more stringent desired characteristics in a cost effective manner, the amount of information and processing that occurs on a typical vehicle is staggering. Most vehicles today have well over 50 processors on board, and the number continues to grow. Indeed, electronics can account for over 40% of the vehicle’s cost and this percentage will continue to grow. Onboard systems are only part of the explosion of automotive informatics and commutations. Infrastructure -to-vehicle and vehicle-to-vehicle communicants are enabling a host of new frontiers related to safety, traffic control and maintenance. Onboard navigation systems can now route an individual vehicle through significant traffic jams or disruptions. However, in the near future, coordinated efforts between the traffic infrastructure and multiple vehicles may distribute the traffic load to minimize congestion or the effects of construction or a traffic accident. Furthermore, information from adjacent vehicles may be used to avoid collisions. For example, vehicles that are rapidly decelerating on a highway might warn subsequent cars of an impending “stopped traffic hazard.” From a maintenance perspective, connectivity has already enabled the vehicle to communicate its health status and potential failures to service personnel. Such information is not only critical to keep a vehicle functioning properly, but also enables fleet manufacturers to track potential problems, and address them as rapidly as possible. Furthermore, this information can easily and rapidly be utilized in improving next generation vehicles.
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For both onboard systems and supporting infrastructure systems, the acceleration of technological change is driving vehicle designers, manufacturers and consumers to rethink how the automobile is developed from conceptualization to production to service to end of life. The rapidly changing electronics and informatics sector has pushed vehicle system design and integration to a new level of agility. The consumer desires state-of-the-art capabilities, and automobile producers no longer have several years to incorporate the latest technology into their products. This is fostering a change in the way vehicles are designed and perceived. Indeed, if one looks at the automobile, it is changing rapidly and the pace of change is ever increasing. The car of today is vastly different from its predecessors of 30 or 40 years ago, and next generation vehicles will continue to change dramatically driven by multiple issues of which many are related to informatics and communication systems. This book has provided fundamental principles, as well as practice, and new research/trend for vehicular networks and advanced information technologies applied in the automotive area. First, this book presents the impact of drive-by-wire systems on vehicle safety and performance, and electromagnetic compatibility issues affecting automotive communications. It then introduces Intra-vehicle networks like LIN (Local Interconnect Network), CAN (Controller Area Network), MOST (Media-Oriented Systems Transport), Flexray, powerline communication, and so forth. It also describes in-vehicle network architecture for the next-generation vehicles and elaborates the potential applications and related technical challenges in achieving secure remote monitoring and control of vehicles via CAN. Second, this book presents the technologies related to Vehicle-to-Vehicle, and Vehicle-to-Infrastructure communications by describing the current medium access control (MAC) and routing protocols for vehicular networks, and the role of communications in cyber-physical vehicle applications. Furthermore, it incorporates the characteristics of traffic flow into the interference issue in the communication layer of VANETs (Vehicular Ad Hoc Networks), and presents new research into proactive traffic merging algorithms and the potential benefits of applying sensor-enabled cars. The book has also captured the state-of-the-art in the area of traffic control with the assistance of VANETs, and reviewed the problem of estimating in real-time the position of a vehicle for use in land navigation system. Last but not least, privacy, security and reliability as key requirements in deploying VANETs are addressed, as well as simulation architectures and simulation tools implementation methods with the aim to improve the traffic safety and control. Through all chapters, this book has discussed the future trends for the automotive informatics and communicative systems in each individual domain. I highly recommend Dr. Guo’s timely book. I believe it will benefit many readers and be a good reference. Thomas R. Kurfess International Center for Automotive Research, Clemson University, USA
Thomas R. Kurfess received his SB, SM and PhD degrees in mechanical engineering from M.I.T. in 1986, 1987 and 1989, respectively. He also received an SM degree from MIT in electrical engineering and computer science in 1988. Following graduation, he joined Carnegie Mellon University where he rose to the rank of associate professor. In 1994 he moved to the Georgia Institute of Technology where he rose to the rank of Professor in the George W. Woodruff School of Mechanical Engineering. In 2005 he was named Professor and BMW Chair of Manufacturing in the Department of Mechanical Engineering at Clemson University. He is also the Director of the Campbell Graduate Engineering Center at Clemson University’s International Center for Automotive Research. He has served as a special consultant of the United Nations to the Government of Malaysia in the area of applied mechatronics and manufacturing, and as a participating guest at the Lawrence Livermore National Laboratory in their Precision Engineering Program. His research focuses on the design and development of advanced systems targeting the automotive sector (OEM and supplier) including vehicle and production systems. He has signi.cant experience in high precision manufacturing and metrology systems. He has received numerous awards including a National Science Foundation (NSF) Young Investigator Award, an NSF Presidential Faculty Fellowship Award, the ASME Pi Tau Sigma Award, SME Young Manufacturing Engineer of the Year Award, the ASME Blackall Machine Tool and Gage Award, the ASME Gustus L. Larson Award. He is a Fellow of the SME and of the ASME.
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Preface
The automotive industry is undergoing a continuous transformation; vehicles are no longer thermo mechanical systems with some electronic components used to start engines and lighting. Today’s vehicles are complex systems, with networks of computers controlling their most important functions. Increasing fuel costs, as well as increasing awareness of vehicular pollution and noise affecting large human agglomerations and unacceptable numbers of traffic accidents and road congestion are exerting much pressure for change on the automotive industry. What kind of change is expected? Within a short period, mobile communications have changed our lifestyles allowing us to exchange information, almost anywhere at anytime. The introduction of such mobile communications systems in motor vehicles should be therefore only a matter of time. This should bring a new paradigm, that of sharing information amongst vehicles and infrastructure, and lead to numerous applications for safety, traffic efficiency as well as infotainment. The main purpose of this book is to provide an overview of the information and communications technologies that are to be deployed in the new generations of vehicles – to provide valuable insights into the technologies for vehicular networks and data exchange, from both theoretical and practical perspectives. We hope that the contents can be used in graduate level courses as a reference and by the automotive industry as training material. The book should provide a concise background and a good foundation to students entering the field of automotive information and communications technologies. We also hope that it would serve as a reference to researchers/scientists and practitioners by enabling them to offer exciting and novel technologies and applications that would, in the future, transform our land transportation systems. Information technology is the driving force behind innovations in the automotive industry. In the past years, control systems of cars have moved from the analog to the digital domain. In particular, xby-wire systems began to appear, and have driven research efforts of the whole automotive industry in the last decade. Networked Electronic Control Units (ECUs) are increasingly being deployed in cars to realize diverse functions such as engine management, air-bag deployment, and even in intelligent brake systems. At the same time, emerging vehicular networks in the forms of intra-vehicle, vehicle-to-vehicle and vehicle-to-infrastructure communications are fast becoming a reality. They will enable a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment to be incorporated into modern automobile designs. This book introduces the advanced information technologies that shape the ultra-modern automotive industry today. Contributions to this publication are made by professors, researchers, scientists and practitioners throughout the world, bringing together their rich expertise and results of their current endeavors. The authors have several years of expertise in their respective domains, and have good publication records.
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As can be seen from the table of contents, the book comprises of 15 chapters. It spreads across many technical areas with car communications as the central theme. The first chapter is an introductory chapter on the emerging area of vehicular networks in the forms of Intra-Vehicle (InV), Vehicle-to-Vehicle (V2V), and Vehicle-to-Infrastructure (V2I) communications. Chapters II to III and a large part of Chapter V cover the technologies related to InV networks. The rest of Chapter V and Chapters VI to XII present the technologies related to V2V and V2I communications which will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment. Privacy, security, and reliability as key requirements in deploying VANETs (Vehicular Ad Hoc Networks) are addresses in Chapter XIII. Chapter XIV discusses simulation architectures, models used for representing the communication among vehicles, vehicle mobility features, and ways to implement simulation tools with the aim to improve traffic safety and control. Chapter XV describes in-vehicle network architectures for the next-generation vehicles. Chapter-wise details are presented bellow. The introductory chapter presents the emerging area of vehicular networks in the forms of Intra-Vehicle (InV), Vehicle-to-Vehicle (V2V), and Vehicle-to-Infrastructure (V2I) communications. This will enable a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment, to be incorporated into modern automotive designs. Critical data is being exchanged within a vehicle and with outside the vehicle via vehicular networks. Thus, this chapter first introduces car communications, potential vehicular applications and wireless technologies, as well as specially designed technologies DSRC (Dedicated Short Range Communications) standards and communication stack for data exchange. As the emerging area of vehicular networks is attracting widespread interest from research groups around the world, this chapter next introduces the consortiums and initiatives working on advanced automotive technologies in Europe, the United States, Japan, and Singapore. Finally, in the future trend, vehicular networks still plays a vital role in enhancing the automotive industry for safety, security and entertainment. Chapter II presents the impact of drive by wire systems on vehicle safety and performance. An overview of the drive-by-wire technology is presented along with in-depth coverage of salient drive by systems such as throttle-by-wire, brake-by-wire, and steer-by-wire systems, and hybrid-electric propulsion. This is followed by in-depth coverage of technological challenges and the current state-of-the-art solutions to these technological hurdles. For example, an analytical redundancy/model-based faulttolerant control can not only reduce the overall system cost by reducing the total number of redundant components, but also further improve overall reliability of the system through the usage of a diverse array of sensory information. Future trends in the drive-by-wire systems include various drive-by-wire systems in the same vehicle sharing a diversity of sensors and actuators via data fusion methodologies, integrated control of various drive by wire systems and future communication bus for x-by-wire systems, for example FlexRay, Chapter III provides a basic overview of the electromagnetic compatibility (EMC) issues affecting automotive communications. As the number of electronic systems in automobiles rises, the potential for electromagnetic interference increases. Designing for electromagnetic compatibility is important to devote proper attention to electromagnetic compatibility at every stage of an automobile’s development. Problems discovered late in the design cycle can seriously impact development schedules and product cost. This chapter provides basic information of electromagnetic compatibility issues affecting automotive communications for non-EMC engineers and engineering managers who work with automotive networks. Chapter IV introduces the most widely used automotive networks like LIN (Local Interconnect Network), CAN (Controller Area Network), MOST (Media-Oriented Systems Transport), and FlexRay. To fulfill the increasing demand of intra-vehicle communications, a new technique based on power
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line communication (PLC) is then proposed. This allows the transmission of both power and messages without functional barriers. On the other hand, there are several infotainment applications (like mobile phones, laptop computers) pushing for the adoption of intra-vehicle wireless communications. Thus, some potential wireless technologies used in the automotive domain, namely Bluetooth, IEEE 802.11 b/g wireless technology – WiFi, and Zigbee are covered here. Finally, the chapter highlights the challenges of these wired or wireless alternative solutions in automotive networks. Chapter V elaborates one of the most popular in-vehicle networking technologies called Controller Area Network (CAN). The chapter begins with an overview of the basis and the general technology of CAN in automotive industry and the deployment of in-vehicle CAN networks. It then presents the various existing and future potential applications that make use of the CAN data, and the related technical challenges in achieving secure remote monitoring and control of vehicles via CAN. Furthermore, the chapter elaborates two key components in achieving remote vehicle monitoring and control, namely, the wireless communication component and data security component. It stresses the importance of secure data and information flow between vehicles and an application server. Finally, the chapter presents an overall architecture for secure wireless real-time vehicle monitoring and control environment. In future rends, the author foresees the area of real-time monitoring and control being a fertile ground for future automotive innovations and services. Chapter VI describes the current medium access control (MAC) and routing protocols for vehicular networks, and the various factors that affect their design and performance. The mobility and speed of the communicating nodes in vehicular networks add extra dimensions to the challenges faced by the MAC protocols, in addition to the existing requirements of reliability and efficiency. This chapter reviews some of the existing MAC protocols for vehicular network. For example, basic MAC protocols, the IEEE 802.11 MAC Extension for Vehicular Networks, and other MAC Protocols for Vehicular Networks (ADHOC-MAC, the Directional MAC (D-MAC) protocol). Future Intelligent Transportation Systems require fast and reliable communication between cars (vehicle-to-vehicle) or between a car and a road side unit (vehicle-to-infrastructure). Ad hoc unicast routing schemes can be divided into two categories: topology-based routing and position-based routing. Topology-based schemes use a variety of proactive routing schemes (DSDV ((Destination Sequenced Distance Vector routing), Optimized Link State Routing (OLSR), Fisheye State Routing (FSR)) or reactive approaches (AODV (Ad Hoc On-Demand Distance Vector Routing), DSR ((Dynamic Source Routing), Temporally Ordered Routing Algorithm (TORA), Associativity Based Routing Algorithm (ABR), or hierarchical protocols (Cluster Based Routing Protocol (CBRP), Core Extraction Distributed Ad-hoc Routing (CEDAR) and Zone Routing Protocol (ZRP)) to create routes. Popular location services in position-based routing protocols are Distance Routing Effect Algorithm for Mobility (DREAM) and Grid Location Service (GLS). Context Assisted Routing (CAR) and Spatially Aware Routing (SAR) are proposed routing algorithms to overcome the problem of topology holes in position-based routing. Current multicast protocols that can be used in V2V networks include: Position-based Multicast (LBM), GeoGRID, Unicast Routing with Area Delivery and Inter-Vehicle Geocast. Overall, routing of communications for vehicular safety applications remains a challenging topic. Chapter VII presents a new reactive algorithm based on location information in the context of vehicular ad-hoc networks. It proposes a Location Routing Algorithm with Cluster-Based Flooding (LORA-CBF), which is formed with one cluster head, zero or more members in every cluster, and one or more gateways to communicate with other cluster heads. It first validates the model at one, two, and three hops by comparing the results of the test bed with the results of the model developed in OPNET. For more than three hops, it validates the model by comparing with two non-position-based routing algorithms (AODV and DSR) and one position-based routing algorithm (GPSR (Greedy Perimeter
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Stateless Routing)). Results show that mobility and network size affects the performance of AODV and DSR more significantly than LORA_CBF and GPSR. It is also observed that GPSR and LORA-CBF behave similarly in terms of the end-to-end delay, and LORA_CBF is more robust in terms of delivery ratio, routing overhead, route discovery time, and routing load compared with GPSR. Chapter VIII presents the role of communications in cyber-physical vehicle applications. Cyber-physical systems use sensing, communications and computing to control the operation of physical devices. The embedded computers and sensors both within the vehicles and in the infrastructure will be networked into cyber-physical systems to reduce accidents, improve fuel efficiency, increase the capacity of the transportation infrastructure, and reduce commute time. Communications between nearby vehicles will enable cooperative control paradigms that reduce accidents more than computing and sensors alone, and communications between vehicles and the infrastructure will improve the scheduling of traffic signals and route planning. The chapter describes applications that improve the operation of automobiles, control traffic lights and distribute the load on roadways. The requirements on the communications protocols that implement the applications are determined and a new communications paradigm, neighborcast, is described. Neighborcast communicates between nearby entities, and is particularly well suited to transportation applications. Chapter IX incorporates the characteristics of traffic flow into the interference issue at the communication layer of VANETs. There are several fundamental issues, such as connectivity, reachability, interference and capacity, with respect to information propagation in VANETs. This chapter mainly addresses the issue of interference, by incorporating the characteristics of traffic into this issue at the communication layer of VANETs. High node mobility and dynamic traffic features make the interference problem in VANETs quite different. As compared with previous efforts to solve this problem which only considered static network topologies, this work is (to the best of our knowledge), the first to demonstrate the interference features in VANETs by incorporating realistic traffic flow characteristics based on a validated simulation model. Analytical expressions are developed to evaluate the interference in VANETs taking account of both the macroscopic and the microscopic traffic flow characteristics. These analytical expressions are validated within the simulation framework. The results show that the analytical characterization performs very well to capture the interference in VANETs. The results from this work can facilitate the development of better algorithms for maximizing throughput in VANETs, and the research efforts bridging the features of both the communication layer and the transportation layer will help to build more efficient systems. Chapter X first captures the state-of-the-art in the area of traffic control with the assistance of VANETs in terms of vehicular traffic models, vehicular traffic theories, flow control strategies, and performance measurement methodologies. It surveys traffic control strategies for optimizing traffic flow on highways, with a focus on more adaptive and flexible strategies facilitated by current advancements in sensor-enabled cars and VANETs. It provides an overview of new ideas and approaches in the area of traffic flow control with the assistance of VANETs. This chapter then presents new research into proactive traffic merging strategies and the potential benefits of applying sensor-enabled cars. It shows how sensor-enabled cars can assist in improving merging algorithms, and compares proactive merging algorithms against a conventional merging strategy: priority-based merging. Assisted by advanced sensing and communication technologies, traffic control strategies and merging algorithms will lead to more efficient use of the current road networks and ultimately help to alleviate traffic congestion. It has shown that the significant improvement in traffic flow and the decrease in travel time mainly result from the decoupling of the merging point and the decision point, and multilane optimizations, such as pre-lane-changing. Proactive merging strategies can significantly improve traffic flow by increasing it by up to 100% and reduce overall travel delay by 30%.
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Chapter XI presents the localization problem in cooperative vehicle applications by focusing on the constraints imposed by the need for precise vehicle localization estimates. Such accurate estimates are a pre-requisite to deploying vehicles with adequate communications capabilities in real traffic conditions. V2V and V2I safety applications are complex, and the problem does not limit to communications capabilities. It involves spatial information with respect to the vehicles’ locations relative to each other and the infrastructure. Thus, V2V and V2I applications are considered as a spatio-temporal problem. The tenet is that information can be shared only if this is time stamped and related to a spatial description of the information sources. The chapter formulates the spatio-temporal problem having as constraint the precision of the pose estimates of the vehicles involved. It formulates the localization problem and accuracy of digital road maps as a combined issue that needs to be addressed for the successful deployment of cooperative vehicle applications. The problem formulation is completed by two case studies, the use of V2V or V2I communications to traverse safely an intersection and an overtaking manoeuvre. The chapter concludes by including comments and recommendations on the precision limits of the vehicle pose estimations and the potential uncertainties that need to be considered when designing V2V and V2I applications. Chapter XII reviews the problem of estimating (in real-time) the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, it looks at the mathematical framework and technologies involved in the design of positioning systems. Through a review of some of the various sensor fusion techniques usually encountered in such systems, it compares the performance of some of the most popular data fusion approaches, and provides some insights on their limitations and capabilities. The extended Kalman filter (EKF) in data fusion centralized architectures remains a design of choice for most applications. The chapter then describes how to make positioning systems more robust and adaptive by detecting and identifying sensor faults. Finally, it explores possible architectures for collaborative positioning systems, where many vehicles are interacting and exchanging data to improve their own position estimate using a collaborative and geometric data fusion approach. One major trend seen in the field of dense sensor networks is in the use of multilateration techniques for location accuracy. Despite significant errors in range estimates between sensors, multilateration is able to render more accurate location estimates, thus making it suitable for use in vehicle navigation. With the current evolution of automotive technologies, all vehicles are becoming networked and equipped with wireless communication capabilities, thus allowing the use of distributed and collaborative techniques for navigation and positioning. Wireless communications networks are becoming attractive to localize vehicles using various radio-based range technologies such as received signal strength indicators (RSSI), power signal attenuation or time-of-arrival (TOA) techniques. Privacy, security, and reliability as key requirements in deploying VANETs are addressed in Chapter XIII. Without these strengths, the VANET technology will not be suitable for market diffusion. This chapter concerns with how to fulfill these requirements by using pseudonym-based authentication, and designing security schemes that do not endanger transport safety while maintaining low overhead. At the same time, the design improves system usability by allowing nodes to self-generate their own pseudonyms. It manages security credentials in VANET through self-generation and self-certification of pseudonyms, which greatly simplifies the security management and makes a step towards a usable system. It employs group signatures to generate certificates which satisfy the requirements of anonymity and liability attribution, and results show that the computational cost and the overhead are comparable to the baseline approach. Next, it analyzes the costs imposed by security on the transportation systems by analyzing data link performance to obtain packet reception probability curves for the pseudonymbased security systems, and analyzing the impact of safety messaging, security and privacy-enabling technologies on transportation safety to show that secure communication schemes achieve safety levels
xx
comparable to those with no security at all. This chapter performs a detailed investigation of pseudonymbased authentication by analyzing several system issues and showing how these security mechanisms can be applied in practice. Chapter XIV systematically presents actual issues faced by developers and engineers in the simulation of VANET applications, some of which are related to the challenges in developing VANET simulators. It discusses simulation architectures, models used for representing the communication among vehicles, vehicles mobility features, and simulation tools implementation methods. The focus is on the new trends in communication protocols and traffic models, and on new facilities incorporated in simulation tools. Advances in VANET technology and protocols support the adoption and use of more complex mobility models and of more flexible and adaptable traffic controls. VANETs’ rapid topology changes or the changes in the vehicles mobility as reaction to traffic changes are captured by the simulation models, which become more or less complicated and include more elements that constrain vehicle mobility: maps, real traffic conditions (congestion), driver behavior, fuel consumption, pollutant emissions, and so forth. It also includes a critical analysis of the solutions adopted in some well-known actual simulators. Other issues related to the use of simulation in the evaluation of applications that aim at improving traffic safety and control are discussed. Representative city and highway application scenarios are analyzed, and results obtained by simulation, along with ways these results can be exploited by VANET developers and users, are highlighted. Future trends in the development of simulators that produce more accurate results, and their use for the evaluation of more sophisticated traffic control solutions, are also included. Chapter XV takes a more futuristic look at various types of topologies and protocols that could be used specifically in in-vehicle networks. Varying functionalities of vehicles will require different types of communication networks and networking protocols. As the size and complexity of the network grows, integration, maintenance and troubleshooting will become a major challenge. To facilitate integration and troubleshooting of various nodes and networks, it would be desirable that networks of future vehicles be partitioned, and the partitions be interconnected by a hierarchical or multi-layer physical network. These partitions must be appropriately interconnected to handle functional dependencies and for better diagnostics. A number of network topologies have been presented and analyzed for cost, bandwidth and message latencies. This chapter describes a number of ways using which the networks of future vehicles could be designed and implemented in a cost-effective manner. Since future vehicles will also be communicating with external entities for various reasons, the chapter also addresses the issues of security, safety and privacy which should be taken into consideration at the time of designing the in-vehicle network components. Finally, some ideas have been presented in developing simulation models to analyze various types of networks which will ultimately help in selecting the most appropriate network topology and various network components for a given set of requirements and specifications. Thus, we have walked through the world of new information and communication technologies being developed for vehicular systems. We hope that the book is of interest to academia and industry. We earnestly hope that the insights provided by this book, on the specific information and communication technologies used in vehicles, will help inspire and spawn a multitude of novel applications and innovations. This book is dedicated to my parents Lanying Guo and Tianfu Guo. Huaqun Guo Singapore,October 2008
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Acknowledgment
I would like to extend my utmost gratitude to the people who have, in one way or other, inspired, aided and contributed to the successful completion of this book. First of all, I would like to express my sincere gratitude to Deputy Executive Director (Research) Professor Lye Kin Mun at the Institute for Infocomm Research (I2R) for his strong encouragement of my editing works. I would also like to thank the A*CAR (A*STAR Capabilities for Automotive Research) taskforce, and the management of Institute for Infocomm Research (I2R) for providing me the opportunity to carry out research into the exciting area of vehicular networks. In particular, I would like to thank Dr. Feng Bao and Dr. Yongdong Wu at I2R for their support. Special thanks to all reviewers for their expertise, time, effort and timely response throughout the peer evaluation process. In particular, I wish to express my appreciation to the members of the Editorial Advisory Board for their guidance, support and constant encouragement. I would also like to take this opportunity to thank Tyler Heath and Heather A. Probst for their assistance with this book. Last but not least, the heartiest gratitude is given to my family for their love and encouragement. Huaqun Guo Institute for Infocomm Research, A*STAR, Singapore
Chapter I
Introduction:
An Emerging Area of Vehicular Networks and Data Exchange Huaqun Guo Institute for Infocomm Research, A*STAR, Singapore
ABSTRtract Emerging vehicular networks in the forms of Intra-Vehicle (InV), Vehicle-to-Vehicle (V2V), and Vehicleto-Infrastructure (V2I) communications will enable a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment to be incorporated into modern automobile designs. At the same time, networked Electronic Control Units (ECUs) are increasingly being deployed in automobiles to realize functions such as engine management, air-bag deployment, and even in intelligent brake systems. In addition, users now expect to sit in an automobile and have their brought-in devices, and beamed-in services harmoniously integrated with the built-in interfaces inside the automobile. Thus, widespread adoption of vehicular networks is fast becoming a reality and critical data is being exchanged with-inside and with-outside vehicle via vehicular networks. This chapter gives an overview of this emerging area of vehicular networks, its potential applications, its potential wireless technologies for data exchange, and its research activities in the Europe, the United States (U.S.), Japan, and Singapore.
INTRODUCTION In recent years, control systems of automobiles have moved from the analog to the digital domain. In particular, x-by-wire systems are appearing and have driven research efforts of the whole automotive industry for the recent decade. Networked Electronic Control Units (ECUs) are
increasingly being deployed in automobiles to realize diverse functions such as engine management, air-bag deployment, and even in intelligent brake systems. For example, at least 70 networked ECUs are employed in a Mercedes S-Class car (Heffernan & Leen, 2008; Vasilash, 2005). At the same time, emerging vehicular networks in the forms of Intra-Vehicle (InV), Vehicle-to-Vehicle
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Introduction
(V2V), and Vehicle-to-Infrastructure (V2I) communications are fast becoming a reality and will enable a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment to be incorporated into modern automobile designs. There are currently a number of study groups working on car communications and defining the standards for various applications. InV Communications, such as CAN (Controller Area Network, 2008; CiA, 2008), LIN (Local Interconnect Network, 2008), FlexRay (2008), are used for interconnecting in-car ECUs, sensors, and so on. V2V Communications, such as IEEE 802.11p (IEEE 802.11p, 2008; Jiang & Delgrossi, 2008), Dedicated Short Range Communications (DSRC) (Dedicated Short Range Communications, 2008), may be used for safety applications. V2I communications, e.g. IEEE 802.11p and IEEE 1609 Family of Standards for Wireless Access in Vehicular Environments (WAVE, 2008) may be used for traffic information. In addition, users now expect to sit in an automobile and have their brought-in devices and beamed-in services harmoniously integrated with the built-in interfaces inside the automobile. To integrate mobile phones and digital music players, Ford designs Ford Sync that integrates voice-activated in-car communication and entertainment system (Ford Sync, 2008). RM MICHAELIDES provides wireless CAN interfaces to transmit CAN between different networks using Bluetooth, RFID (radio-frequency identification), Infrared, UHF (ultra high frequency), etc (Michaelides, 2008). A Controller Area Network Gateway to ZigBee was described in (Kuban, 2007). There are some wireless CAN products, such as CANRF (Dammeyer, 2008) and CAN Bridge (Matric, 2008). The performance of wireless CAN in terms of latency and throughput was studied in (Dridi, Gouissem, Hasnaoui, & Rezig, 2006). Thus, with vehicular networks fast becoming commonplace, critical data is being exchanged with-inside and with-outside vehicle via vehicu-
lar networks, and new technologies have been developed for vehicular networks. This chapter is meant to introduce the emerging area of vehicular networks and data exchange, give an overview of the new technologies for car communications, and present automotive research activities in the Europe, the United States (the U.S.), and Japan as well as in Singapore.
CAR COMMUNICATIONS New technologies are being developed for vehicular networks and these networks provide an efficient method for today’s complex car communications. Figure 1 shows the example of InV, V2V and V2I communications. InV provides communication among ECUs/ sensors in a vehicle while V2V and V2I provide communications among nearby vehicles and between vehicles and nearby fixed roadside equipments. Vehicular networks are a cornerstone of the envisioned Intelligent Transportation Systems (ITS). By enabling vehicles to communicate with its function systems via InV communication, with other vehicles via V2V communication as well as with roadside base stations via V2I communication, vehicular networks will contribute to safer and more efficient roads by providing timely information to drivers and concerned authorities.
Potential Applications The emerging vehicular networks will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment: 1.
Safety: Vehicular network technologies will be applied to reduce accidents so as to save lives and reduce injuries. Examples of such applications include vehicle breakdown and obstacle detection, lane departure warning, accident warnings, collision warning,
Introduction
Figure 1. Example of InV, V2V, and V2I communications
ECU
ECU ECU ECU
CAN BUS
2.
3.
4.
ECU
ECU ECU
over-height / over-width warning, turnover warning, work zone warnings, black box, and so on. Traffic Efficiency: Vehicular network technologies will be applied to improve the flow of traffic and reduce congestion, for example, cooperative adaptive cruise control, highway/rail intersection traffic management, congestion information for traffic control, electronic toll collection, etc. Driver Assistance: Vehicular networks can also provide accurate information and data, as well as good communications for drivers to improve safety and security, e.g., digital road maps downloading, advanced navigation system, parking information, realtime traffic information, various warning information, driver’s daily blog, automatic emergency call, etc. Infotainment: Vehicles in future are foreseen to be dominated with feature-rich audio-video infotainment. Users can expect to sit in an automobile and have their brought-in devices and beamed-in services harmoniously integrated with the built-in interfaces
inside the automobile. Furthermore, users can also wirelessly purchase and synchronize the latest movies and songs when they top up fuel at the kiosk. Last but not least, wide range of applications for entertainment through Internet will be brought to automotive passengers.
Potential Wireless Technologies With the rapid development of information technologies, there are a number of wireless technologies which are potential for wireless InV, V2V and V2I communications, and listed in Table 1 and Table 2. These new technologies could be used for data exchange between users’ devices and vehicles, among vehicles, and between vehicles and infrastructure. The details of application scenarios of data exchange with each technology are listed in two tables as well. DSRC is the recent technological trends to provide real time traffic information for effective implementation of ITS. Thus, in the following subsection, we focus on introducing the new technologies of DSRC for vehicular networks.
Introduction
Table 1. Wireless technologies for InV communications ZigBee Standard/ Technology
UWB (ultra-wide band)
Bluetooth
Wireless USB (Universal Serial Port)
Wireless CAN
Ratified in December 2004
Transmitting information spread over a large bandwidth (>500 MHz)
First launched (1998)
Short-range, highbandwidth based on the WiMedia Alliance’s UWB
CANRF (CAN over RF)/ CAN Bridge
Coverage
10 and 75 meters
< 60 cm for a 500 MHz wide pulse, < 23 cm for a 1.3 GHz bandwidth pulse
1 meter, 10 meters, 100 meters
480 Mbit/s at up to 3 meters and 110 Mbit/ s at up to 10 meters
/ 500 feet (152.4M)
Bit Rate
20-250 kbit/s per channel
extremely high data rates 1000+ Mbps
3 Mbit/s (Version 2.0 + EDR) 53-480 Mbps (WiMedia Alliance (proposed)
480 Mbit/s at distances up to 3 meters and 110 Mbit/s at up to 10 meters
20kbps/ 52.8kbps-164.4kbps
Entertainment, smart Lighting control, advanced temperature control, safety & security, sensors, etc
Used at very low energy levels for short-range highbandwidth communications by using a larger portion of the radio spectrum
Connect and exchange information between devices such as mobile phones, laptops, personal computers, video game consoles, etc
Game controllers, digital cameras, MP3 players, hard disks and flash drives. Also suitable for transferring parallel video streams.
Communication among sensors and ECUs
Applications
Table 2. Wireless Technologies for V2V & V2I communications GSM/3G
Standard/ Technology
WiFi (Wi-Fi Alliance Version of 802.11n)
DSRC
Third generation cellular technology in 2001
New Wi-Fi technology with MIMO standard in 2009, 802.11n standard in 2009
Broadband technology in 2007
A short to medium range communications
Coverage
kilometers
500m
5 km
1000m
Bit Rate
2-3 Mbps
600Mbps using MIMO
75Mbps
6 to 27 Mbps
Between Vehicle and mobile phone communication
Roadside to vehicle and vehicle to vehicle communication
Internet access, Email, VoIP (Voice over IP)
Roadside to vehicle and vehicle to vehicle communication
Applications
Dedicated Short Range Communications DSRC is a short to medium range wireless protocol specifically designed for automotive use. It supports both public safety and private operations for V2V and V2I communication environments. DSRC is a complement to cellular communications by providing very high data transfer rates in circumstances where minimizing latency in the communication link and isolating relatively small
WiMax
communication zones are important (Armstrong, 2008). This technology for ITS applications is working in the 5.9 GHz band (the U.S.) or 5.8 GHz band (Japan & Europe). DSRC standards and communication stack are shown in Figure 2 (Jiang & Delgrossi, 2008).
IEEE 802.11p IEEE 802.11p is a draft amendment to the IEEE 802.11 standard to add wireless access in the
Introduction
vehicular environment. It defines enhancements to 802.11 required to support ITS applications. This includes data exchange between high-speed vehicles and between the vehicles and the roadside infrastructure in the licensed ITS band of 5.9 GHz (5.850-5.925 GHz) (IEEE 802.11p, 2008). IEEE 802.11p supports physical layer management entity (PLME), lower MAC (Medium Access Control) layer management entity (L_MLME), Wireless Access in Vehicular Environments physical layer (WAVE PHY) as well as WAVE lower MAC in DSRC technology. The 802.11p Task Group is still active, resolving comments on Draft 3.0, and the final approval is expected in March 2009.
IEEE 1609—Family of Standards for Wireless Access in Vehicular Environments (WAVE)
and interfaces that collectively enable secure V2V and V2I wireless communications. Together these standards provide the foundation for a broad range of applications in the transportation environment, including vehicle safety, automated tolling, enhanced navigation, traffic management and many others. The IEEE 1609 Family of Standards for Wireless Access in Vehicular Environments (WAVE) consists of IEEE P1609.1—Standard for Wireless Access in Vehicular Environments (WAVE)—Resource Manager, IEEE P1609.2—Standard for Wireless Access in Vehicular Environments (WAVE)—Security Services for Applications and Management Messages, IEEE P1609.3—Standard for Wireless Access in Vehicular Environments (WAVE)—Networking Services, and IEEE P1609.4—Standard for Wireless Access in Vehicular Environments (WAVE)—Multi-Channel Operations (WAVE, 2008).
The WAVE standards define an architecture and a complementary, standardized set of services
Figure 2. DSRC standards and communication stack Safety Applications WME IEEE P1609.3
Non- Safety Applications WSMP IEEE 1609.3
UDP
TCP
Security IEEE P1609.2
IP LLC U_MLME IEEE P1609.4
WAVE upper MAC IEEE P1609.4
L_MLME IEEE 802.11p
WAVE lower MAC IEEE 802.11p
PLME IEEE 802.11p
WAVE PHY IEEE 802.11p
Introduction
CONonortium and INITIAnitiati Vehicular networks, as an emerging area, attract a number of R&D groups in the Europe, the U.S., Japan and Singapore to work on the new technologies.
Car 2 Car Communication Consortium The Car 2 Car Communication Consortium (2008) is initiated by European vehicle manufacturers and partners include Audi, BMW, Daimler, Fiat, Honda, Opel, Renault, and Volkswagen. Its objective is to further increase road traffic safety and efficiency by means of inter-vehicle communications. The schedule for the official agreements of consortium is from July 2005 for the basic concept till December 2010 for frequency allocation.
Network on Wheels, Germany Network on Wheels (NoW) (2008) was founded in 2004 and the current partners include Daimler AG, BMW AG, Volkswagen AG, Fraunhofer Institute for Open Communication Systems, NEC Deutschland GmbH, IMST GmbH and embedded wireless GmbH. Besides the partners, the Universities of Mannheim, Karlsruhe and Munich and the Carmeq GmbH also cooperate within NoW. NoW is a German research project which is supported by Federal Ministry of Education and Research. Its main objectives are to solve technical problems on communication protocols and data security for car-to-car communications and to submit the results to the Car 2 Car Communication Consortium. On May 8, 2008, NoW presented its results in a final workshop at the Daimler Research & Development Center in Ulm (Germany).
SAFESPOT SAFESPOT (2008) integrated research project was co-funded by the European Commission Information Society Technologies under the
initiatives of the 6th Framework Program. The objective is to understand how intelligent vehicles and intelligent roads can cooperate to produce a breakthrough for road safety based on V2V and V2I communications.
eSafety eSafety (2008), the first pillar of the Intelligent Car Initiative (i2010 Intelligent Car Initiative, 2008), brings together the European Commission, public authorities, industry and other stakeholders with an aim to accelerate the development, deployment and use of Intelligent Vehicle Safety Systems that use information and communication technologies. The main target is to contribute to the European Commission’s 2001 goal of reducing the road fatalities by 50% by 2010 (from 54 000 to 27 000 – between 2001 and 2010) (European Commission, 2008).
PReVET PReVENT (2008) is a European automotive industry activity co-funded by the European Commission to contribute to road safety by developing and demonstrating preventive safety applications and technologies. Membership in its Core Group consists of seven vehicle manufacturers (DaimlerChrysler, BMW, Renault, PSA Peugeot Citroen, Ford, CRF, Volvo Technical Development), four automotive suppliers (Siemens VDO, Delphi, SAGEM and Bosch) and one research institute (INRETS). One goal of PReVENT is also to contribute to the European Commission’s 2001 goal of halving the number of fatalities on Europe’s roads by 2010 as specified in eSafety for Road and Air Transport. IP PReVENT Final Report can be found in PReVENT web site.
EASIS The EASIS (Electronic Architecture and System Engineering for Integrated Safety Systems)
Introduction
(2007), which was part of the European Commission’s 6th Framework Programme launched in 2004, is a partnership of 22 European vehicle manufacturers, automotive suppliers, tool suppliers and research institutes with the aim to develop technologies for the realization of future ISS (Integrated Safety Systems).
SEVEOM SEVECOM (Secure Vehicular Communication) (2008), an EU-funded project launched in 2006, focuses on providing a full definition and implementation of security requirements for vehicular communications. A liaison with security activities in EASIS supported the activities of SEVECOM. Its members include TRIALOG, Bosch, Budapest University of Technology & Economics, Daimler, EPFL, CRF-Fiat Research Center, Katholieke Universiteit Leuven, and Ulm University. The SEVECOM project will end by January 1, 2009.
Vehicle Safety Communications Consortium The Vehicle Safety Communications (VSC) Project was a 2.5 year program started in May 2002. Vehicle Safety Communications Consortium (2008) members, including BMW, DaimlerChrysler, Ford, GM, Nissan, Toyota and Volkswagen, participated with the U.S. Department of Transportation in this cooperative program. The objective was to identify vehicle safety applications enhanced or enabled by external communications, determine their respective communication requirements, evaluate the emerging 5.9 GHz DSRC vehicle communications technology and align the proposed DSRC communications protocols to meet the needs of vehicle safety applications.
US DOT The U.S. Department of Transportation’s (USDOT, 2008) ITS program focuses on intelligent
vehicles, intelligent infrastructure and the creation of an intelligent transportation system through the integration of these two components. The Federal ITS Program Initiatives in 2004 included Vehicle Infrastructure Integration, Cooperative Intersection Collision Avoidance Systems, and Integrated Vehicle Based Safety Systems. Through the Integrated Vehicle-Based Safety Systems initiative, the USDOT is seeking to establish a partnership with the automotive and commercial vehicle industries to accelerate the introduction of integrated vehicle-based safety systems into the Nation’s vehicle fleet.
VII Consortium (VIIC) VII (Vehicle and Infrastructure Integration) Consortium (VIIC) (VIIC and VII Program Overview, 2005; Robinson, 2006) was incorporated in November 2004, and cooperative agreement was signed in December 2005. Current member participation includes Ford, DCX, Nissan, Honda, VW and BMW. The objective is to create an enabling communication infrastructure to save lives using intelligent warning systems, improve mobility and congestion, enhance driving experience with new services and enhance roadway maintenance and planning. VIIC provides single voice to USDOT and joint pre-competitive technology development environment.
California PATH California Partners for Advanced Transit and Highways (California PATH, 2008) was founded in 1986 and is administered by the Institute of Transportation Studies, and University of California, Berkeley, in collaboration with Caltrans (California Department of Transportation). PATH is a multi-disciplinary and cooperative program for staffs, faculties and students from universities statewide to engage in cooperative projects with private industry, state and local agencies, and non-profit institutions. Its mission is to develop
Introduction
solutions to the problems of California’s surface transportation systems. Current PATH research projects are divided into four program areas: Policy and Behavioral Research, Transportation Safety Research, Traffic Operations Research, and Transit Operations Research.
CALM Continuous Communications for Vehicles CALM (2008) is under ISO TC 204 Working Group 16 Wide Area Communications-Protocols and Interfaces. The scope of CALM is to provide a standardized set of air interface protocols and parameters for medium and long range, high speed ITS communication including V2V and V2I communications.
AUTOSAR AUTomotive Open System ARchitecture (AUTOSAR, 2008) is a partnership of automotive manufacturers and suppliers working together to develop and establish an open and standard automotive software architectures. Its core partners are BMW, Bosch, Continental, Daimler, Ford, OPEL, GM, PSA Peugeot Citroën, TOYOTA, and Volkswagen. The AUTOSAR project plan was released in May 2003 and the first AUTOSAR Open Conference held in October 2008.
JasPar JasPar (2008) is formed by the Japanese automotive companies and its board members include TOYOTA MOTOR CORPORATION, Nissan Motor Co., Ltd, Toyota Tsusho Electronics, Honda R&D Co., Ltd and DENSO CORPORATION. The aim of JasPar is to reduce technology development costs and promote technology development by encouraging Japanese companies to collaboratively develop pre-competitive technologies such as automotive LAN enabling technology,
middleware and software platform, and contribute to development of global standards.
Internet ITS Consortium (Japan) Internet ITS Consortium (2008) is developing a common Internet ITS platform and aims for promotion of a standardized global Internet ITS specification. It is formed by Japanese companies and has 11 companies as Executive Members, 12 companies as Regular Members and 69 companies as Supporting Members.
A*CAR in Singapore With the rapid growth in the global automotive population, the A*STAR (Agency for Science, Technology and Research) Capabilities for Automotive Research (A*CAR) began as a taskforce with the aim of establishing an initiative to address technical challenges in the automotive area and provide technical leadership to the automotive supplier industry in Singapore through R&D (Yong, 2008). A*CAR taskforce has launched an automotive consortium to bring together automotive OEMs, suppliers and R&D community to work hand in hand in addressing key research areas in automotive technology. The consortium is driven by 7 A*STAR research institutes namely, Data Storage Institute (DSI), Institute for Infocomm Research (I²R), Institute of Chemical and Engineering Sciences (ICES), Institute of High Performance Computing (IHPC), Institute of Materials Research and Engineering (IMRE), Institute of Microelectronics (IME), and Singapore Institute of Manufacturing Technology (SIMTech) (A*CAR, 2008).
FUTURE TRENDS The global automotive industry is the world’s largest manufacturing industry and most industry
Introduction
analysts predict that there will be a year-on-year growth till 2012 (PwC Automotive Institute, 2008). The overall market for automotive networking will grow at a 10% annual rate through 2011 and the components for the automobile networking market will nearly triple between 2005 and 2011. Driving this trend is the introduction of more electronics in every vehicle, and that accounts for the much faster annual growth rate than the auto industry’s 2 to 3% (Regt, 2007). In the future, more and more control modules will be attached into the high-speed data networking backbones embedded in a vehicle. Vehicular networks will simplify the operation of control systems and allow more features to be deployed within automobile. For example, information and direction displays will be embedded directly into windshields. Another example is that instead of self-parking being a service that is offered only on luxury cars, it will soon become a standard feature on every car. With the advances in sensors, control, GPS (Global Position System) and networking technologies, “self-driving cars” will become possible in the future (Uldrich, 2008). Neurotechnology that studies drivers’ brain patterns and brain computers that read drivers’ intentions from their brainwaves through electroencephalogram (EEG) will help keep them alert and monitor sleepy driver syndrome. Vehicular networks still plays a vital role in enhancing the automotive industry for safety, security and entertainment. In the future, as more and more information is streamed onto the Internet, vehicular networks will allow drivers and passengers to enjoy their journey more than ever before with entertainment and area information.
CONcLUSION This introduction chapter presents the emerging area of vehicular networks in the forms of IntraVehicle (InV), Vehicle-to-Vehicle (V2V), and Ve-
hicle-to-Infrastructure (V2I) communications. It briefly surveys the car communications, potential applications, potential wireless technologies, and specially designed technologies DSRC standards and communication stack for data exchange. As the emerging area of vehicular networks has attracted a number of R&D groups in the world, this chapter then introduces the consortiums and initiatives working on advanced automotive technologies in Europe, the U.S., Japan and Singapore. In the future, vehicular networks certainly play a vital role in enhancing the automotive industry for safety, security and entertainment.
RE Armstrong, L. (2008). What is DSRC? Retrieved September 2, 2008, from http://www.leearmstrong.com/DSRC/DSRCHomeset.htm A*CAR. (2008). Automotive @ A*STAR. Brochure from Agency for Science, Technology and Research (A*STAR). AUTOSAR. (2008). Retrieved September 2, 2008, from http://www.autosar.org California PATH. Retrieved September 2, 2008, from http://www.path.berkeley.edu CALM. (2008). CALM Continuous Communications for Vehicles. Retrieved September 2, 2008, from http://www.calm.hu/ Car 2 Car Communication Consortium. (2008). Retrieved September 2, 2008, from http:// www.car-to-car.org CiA. (2008). CAN-based in-vehicle networks. Retrieved September 2, 2008, from http://www. can-cia.de/index.php?id=228 Controller Area Network. (2008). Retrieved September 2, 2008, from http://www.can-cia.org/ Dammeyer, J. (2008). Wireless Controller Area Network. Retrieved September 2, 2008, from
Introduction
http://www.autoartisans.com/documents/canrf_ prod_announcement.pdf Dedicated Short Range Communications. (2008). Retrieved September 2, 2008, from http://grouper. ieee.org/groups/scc32/dsrc/ Dridi, S., Gouissem, B., Hasnaoui, S., & Rezig, H. (2006). Coupling Latency Time to the Throughput Performance Analysis on Wireless CAN Networks. In Prof. of the International Multi-Conference on Computing in the Global Information Technology (ICCGI’06). EASIS. (2007). Retrieved September 2, 2008, from http://www.easis-online.org eSafety. (2008). Retrieved September 2, 2008, from http://ec.europa.eu/information_society/activities/esafety/index_en.htm European Commission. (2008). European Road Safety Day on 13 October 2008. Retrieved September 2, 2008, from http://ec.europa. eu/transport/roadsafety/road_safety_days/index_2008_en.htm FlexRay. (2008). Retrieved September 2, 2008, from http://www.flexray.com/ Ford Sync. (2008). Today’s Drivers Demand Staying Connected. Retrieved September 2, 2008, from http://media.ford.com/article_display. cfm?article_id=25169 Heffernan, D., & Leen, G. (2008). ICT based research at Limerick contributes to automotive ‘drive-by-wire’ technology. Retrieved September 2, 2008, from http://www.irishscientist. ie/2002/contents.asp?contentxml=02p237b. xml&contentxsl=is02pages.xsl i2010 Intelligent Car Initiative. (2008). Retrieved September 2, 2008, from http://ec.europa.eu/ information_society/activities/intelligentcar/index_en.htm
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IEEE 802.11p. (2008). Retrieved September 2, 2008, from http://en.wikipedia.org/wiki/IEEE_ 802.11p Internet ITS Consortium. (2008). Retrieved September 2, 2008, from http://www.internetits. org/ JasPar. (2008). Retrieved September 2, 2008, from https://www.jaspar.jp/english/index_e.php Jiang, D., & Delgrossi, L. (2008). IEEE 802.11p: Towards an International Standard for wireless Access in Vehicular Environments. Retrieved September 2, 2008, from http://ieeexplore.ieee. org/stamp/stamp.jsp?arnumber=04526014 Kuban, P. A. (2007). A Controller Area Network Gateway to ZigBee- A Proposition of an Architecture to Extend CAN. VDM Verlag Dr. Mueller e.K. Local Interconnect Network. (2008). Retrieved September 2, 2008, from http://www.lin-subbus.org/ Matric. (2008). The Controller Area Network (CAN) has gone wireless! Retrieved September 2, 2008, from http://www.matric.com/resources/ Canbridge_brochureV2.pdf Michaelides, R. (2008). Wireless CAN Interface. Retrieved September 2, 2008, from http://www. rmcan.com/index.php?id=17&L=1 Network on Wheels (NoW). (2008). Retrieved September 2, 2008, from http://www.networkon-wheels.de/ PReVENT. (2008). Retrieved September 2, 2008, from http://www.prevent-ip.org/ PwC Automotive Institute. (2008). Analyst Note, August 1, 2008. Regt, d. H. (2007). Back To Basics & Future Trends: Automotive Networking. December 7. 2007. Retrieved September 8, 2008, from
Introduction
http://www.automotivedesignline.com/howto/204702733 Robinson, R. (2006). The VII Consortium and the Cooperative Agreement with USDOT. Retrieved September 2, 2008, from http://www.itsmichigan. org/AnnualConf2006/presentations/Ralph-ITSM%20Meeting-%20VIIC%20Overview_a.ppt SAFESPOT. (2008). Retrieved September 2, 2008, from http://www.safespot-eu.org/pages/ page.php SEVECOM. (2008). Retrieved September 2, 2008, from http://www.sevecom.org/ Uldrich, J. (2008). The Future of the Automobile. March 10, 2008. Retrieved September 8, 2008, from http://www.jumpthecurve.net/index.php/recent_posts/the_future_of_the_automobile/ USDOT. (2008). Retrieved September 2, 2008, from http://www.its.dot.gov/its_overview.htm
Vasilash, G. S. (2005). Hardware-in-the-loop: Assuring performance & quality by combining the real & the virtual testing. Automotive Design & Production. Gardner Publications, Inc. Retrieved October 14, 2008, from http://findarticles.com/p/ articles/mi_m0KJI/is_/ai_n13784131 Vehicle Safety Communications Consortium. (2008). Retrieved September 2, 2008, from http:// www-nrd.nhtsa.dot.gov/pdf/nrd-12/CAMP3/ pages/VSCC.htm VIIC and VII Program Overview. (2005). Retrieved September 2, 2008, from http://www.leearmstrong. com/DSRC%20Home/Standards%20Programs/ North%20American/Previous%20Meetings/ March%2006/VIIC%20Overview.ppt#1 WAVE. (2008). IEEE 1609—Family of Standards for Wireless Access in Vehicular Environments. Retrieved September 2, 2008, from http://www. standards.its.dot.gov/fact_sheet.asp?f=80 Yong, M. S. (2008). A*CAR. A*STAR Program Proposal.
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12
Chapter II
Drive by Wire Systems: Impact on Vehicle Safety and Performance Sohel Anwar Indiana University-Purdue University Indianapolis, USA
ABSRACT An overview of the drive by wire technology is presented along with in-depth coverage of salient drive by systems such as throttle-by-wire, brake-by-wire, and steer-by-wire systems, and hybrid-electric propulsion. A review of drive by wire system benefits in performance enhancements and vehicle active safety is then discussed. This is followed by in-depth coverage of technological challenges that must be overcome before drive-by-wire systems can be production ready. Current state of the art of possible solutions to these technological hurdles is then discussed. Future trends in the drive-by-wire systems and economic and commercialization aspects of these system are presented at the conclusion of the chapter.
INTRODUCTION Drive by wire (DBW) systems are relatively new technology that are increasingly finding their place in modern automobiles. A drive by wire system is an automotive system that interprets driver’s inputs and executes the commands to produce desired vehicle behavior, typically via a microprocessor-based control system. A typical drive-by-wire system comprises of redundant sensors, actuators, microprocessors, and communication channels for fault tolerance. There
are no mechanical or hydraulic connections between driver’s input interface (e.g. throttle, brake, steering) and vehicle system (e.g. engine/traction motor, brake/steering actuators) in a drive by wire equipped vehicle. A broadened definition of drive by wire systems will include other microprocessor based automotive control systems such as anti-lock braking system (ABS), traction control system (TCS), yaw stability control (YSC), etc. These systems are designed to enhance the safety of the vehicle by continuously monitoring various
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Drive by Wire Systems
vehicle states and taking corrective action and / or warning the driver upon detection of an impending unsafe vehicle condition. The first such system came into commercialization is ABS in early 1970’s. It was followed by traction control system and electronic stability control in the 1980’s and 1990’s (Margolin, 1997; Stanton & Marsden, 1997; Wagstaff, 1999; Davis, 2001; Higgins & Koucky, 2002; Anon, 2003; Fowler, 2003; Ross, 2003; Lee, 2003; Daniels, 2005; Kendall, 2005). The first true drive by wire system to come to the market was Throttle By Wire (TBW) which was incorporated in high end vehicles such as Audi A6, Mercedes Benz, Lexus, and BMW models in the late 1990’s and early 2000’s. The TBW systems were advantageous in stability control applications where the throttle deactivation may be needed in order to improve the traction so that sufficient brake torque can be generated. Electro-hydraulic brake (EHB) system, a form of brake by wire (BBW), was first introduced in Mercedes Benz SL series in 2001-02 (Higgins & Koucky, 2002). Although hydraulically actuated, these brakes operate on commands from sensors at the brake pedal and generate the necessary brake pressure at the wheel cylinders via a set of electronically controlled valves and a pump. However, the brake by wire system was decommissioned and removed from the vehicle due to a number of field problems a few years later. Work on the electro-mechanical brakes (EMB), another form of brake by wire system that does not use hydraulic fluid, was done in the late 1990’s by number of automotive companies such as Bosch, Continental, and TRW. However, issues related to their reliability and fault tolerance still remain which must be addressed before these system can be used in an automobile. Steer by wire (SBW) system is by far the most complex drive by wire system which is also the most safety critical by-wire system in an automobile. In a pure steer by wire system, the steering column is eliminated. Sensors mounted on the steering wheel are interpreted by the controller
to generate the correct amount of road wheel angle using electric motors based on the vehicle velocity. If a sensor stops functioning properly, the controller will not be able to actuate the motors to generate the correct road wheel angle, potentially causing hazardous situation. Figure 1 shows a brief chronology of the drive by wire system introduction into the modern automobile with the broadened definition (Isermann et al, 2002). As shown, the steer by wire system will likely be the last of the drive by wire system to be introduced in the automobile due to its complexity and safety criticality. In an even broader definition, hybrid electric vehicles, electric vehicles, and plug-in hybrid electric vehicles can also be classified as drive by wire equipped automobiles due to the electronic control of various subsystems in these vehicles. Electric vehicles (EV) by their very nature are drive by wire that is propelled by electronic control of the electric traction motor based on the sensor information from the throttle pedal. However, the steering and brakes of an EV may still be hydromechanically operated. In case of hybrid electric vehicle (HEV), a sophisticated microprocessor based control system channels the power flow between the internal combustion (IC) engine, the battery, the electric motor / generator, and the vehicle wheels (Lu & Hedrick, 2005). All of these functions are done via a central controller for optimal performance. Plug-in hybrid electric vehicles are very similar to hybrid electric vehicle, except that a more powerful battery extends the vehicle range in pure electric mode. This chapter is organized as follows: A more detailed coverage on drive by wire system is covered in the next section. The performance and safety benefits of the drive by wire systems are illustrated in the following section. This is followed by the section on technological challenges and possible solutions associated with DBW system. Future trends for the DBW system is presented in the next section. Lastly, some final thought will be presented in the conclusion section.
13
Drive by Wire Systems
Figure 1. Hazard severity of failures in advanced automotive control and drive by wire systems
DRIVE WIRE SYSTEMS: CURRET STATE OF THE ART Figure 2 shows a pictorial view of a number of drive by wire systems in a concept automobile. In addition to standard drive by wire systems, this concept vehicle also includes a 42V converter which is used to power the by-wire systems. With all the drive by wire systems in the vehicle which use electrical power for actuation, the electric power demand must be met using higher voltage systems. 42V power source is thought to be a compromise between high voltage requirement and safety. However, the power demand for hybrid electric vehicles is significantly higher due to propulsion need and hence uses a 240-300V DC power bus. An extra layer of safety in the design of such systems must be incorporated to eliminate the possibility of electrocution. In addition, for fault tolerant architecture, at least two or more such power sources are required for a DBW equipped vehicle. 14
Figure 3 illustrates the first commercialized brake by wire system by Daimler Benz (Higgins & Koucky, 2002). This is an electro-hydraulic brake (EHB) system with mechanical backup that was installed in SL 500 model. The brake pedal displacement sensor output is used to determine the desired wheel cylinder pressure which is generated via a closed loop control system that includes a set of electro-hydraulic valves and an electric motor driven pump. This system was later recalled due to reliability issues. Since then no automakers have incorporated brake by wire systems in any of their vehicles. Figure 4 shows another concept vehicle that incorporates drive by wire systems on a hybrid electric vehicle. This concept is based on the synergy of combining a DBW system with HEV. The DBW systems can easily be powered by the HEV battery pack or the high voltage power bus. The IC engine along with the motor generator will ensure that power is always available for the DBW systems.
Drive by Wire Systems
Figure 2. Concept drive by wire equipped vehicle
Figure 3. Brake by wire equipped Mercedes Benz model SL 500 (source: Daimler Benz)
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Drive by Wire Systems
Figure 4. A drive by wire equipped hybrid electric concept vehicle
Figure 5. General Motors’ HyWire concept vehicle (source: GM)
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Drive by Wire Systems
Another concept vehicle that incorporates drive by wire system in a Fuel Cell HEV (FCHEV) is illustrated in Figure 5. The concept vehicle was designed by General Motors and was codenamed “HyWire” (“Hy” for hydrogen fuel cell and “Wire” for drive by wire). GM later renamed this vehicle “FX-3” and built a prototype of this vehicle in 2006. This vehicle has fuel cell propulsion system with four wheel electric traction motors. Vehicle steering, braking, and suspension are all controlled electronically (drive by wire). Drive-By-Wire systems offer a number of benefits when incorporated on a vehicle. Some of the benefits are as follows: •
•
•
•
•
•
•
DBW systems can easily be configured (via software updates) for added or tunable features such as brake pedal feel / enhanced safety via stability control. DBW systems can enhance vehicle performance by prepositioning brake calipers for fast brake actuation or allowing for oversteering to enhance maneuverability. DBW systems can improve fuel economy via better engine / motor / powertrain control and via regenerative braking. DBW systems can offer better ergonomics such as adjustable feel at driver’s interface (steering wheel, brake / accelerator pedals). DBW systems offer better fault detection and warning to the driver which enhances the safety, reliability, and maintenance of the vehicle. DBW systems are able to incorporate multifunctionality in a single system thereby making today’s advanced features (e.g. stability control systems via active steering) more cost effective in these automobiles. SBW systems can not only provide these additional features, but also can free up premium packaging space by eliminating the steering column thereby enabling easy assembly of the instrument panel.
•
•
•
•
•
•
• •
•
• • •
•
SBW systems also offer variable steering ratio at different vehicle speeds for additional safety. Automatic line keeping is possible for SBW equipped vehicle on the automated highways of the future. This feature will further enhance safety and comfort of the driver. Haptic steering for improved ergonomics and operator safety & performance is possible with SBW vehicles. DBW systems will offer reduced overall production cost for steering systems via standardized modules and software. BBW systems offers improved brake noise, vibration, and harshness (NVH) since there is no direct mechanical or hydraulic link from the pedal to the wheels. ABS pulsation in normal hydraulic brake will disappear in a BBW system. BBW systems also allows for Better Comfort Level ○ Anti-Dive Algorithm Better fuel economy through regenerative braking Easier maintenance ○ Less wear and tear ○ No need to turn those rotors Extended brake life ○ No need to replace brake shoes, pads, or rotors Better cold performance than wet brake systems Better braking-in-a-turn performance Enhanced safety ○ Stability control algorithms ○ Reduction of brake fade Enhanced cruise control on a down hill
However, there are a number of challenges that a DBW system must address before full commercialization. These challenges are discussed in the “Technological Challenges” section. In this section, the following drive by wire systems are presented in more detail:
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Drive by Wire Systems
1. 2. 3.
Throttle By Wire Systems Brake By Wire Systems Steer By wire Systems
Throttle B y Wire (TBW) Systems For traction control system based on engine intervention, it is necessary to control engine torque. By electronically controlling the mass air flow rate via a throttle by wire (TBW) system, it is possible to achieve this functionality. For yaw stability control system, it may be necessary to control the engine torque to the wheels to maximize available traction. TBW systems allow this functionality through their very design. For hybrid electric vehicle propulsion, it is necessary to mechanically disconnect the pedal from the throttle to allow for torque blending between the engine and electric motor which can easily be achieved via a TBW system. A TBW system also provides an easier
way to implement engine braking for commercial vehicle (Jake brake, Exhaust gas brake, etc.). For autonomous vehicles, it is necessary to have a TBW system for vehicle propulsion. Figure 6a illustrates the basic operation of a TBW system. The pedal sensor information along with throttle angle feedback is used to actuate the TBW motor to maintain the commanded throttle angle. Figure 6b shows an electronic throttle body (ETB) used in a TBW system. Figure 7 illustrates a possible interface between ABS / TCS / YSC systems and a TBW system. As indicated earlier, a TBW system is a natural fit in an active safety system or a combination of active systems (as in this case) where upon the ABS / TCS / YSC microprocessors share information with a TBW microprocessor and a supervisory microprocessor. The TBW system microprocessor gives preference to the active safety throttle command over its own pedal command and controls the throttle angle accordingly.
Figure 6a. Schematic of a throttle by wire (TBW) system
Figure 6b. Schematic of an electronic throttle body in a TBW system
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Drive by Wire Systems
Figure 7. TBW system interface with ABS / TCS / YSC active safety systems
Brake B y Wire (BBW) Systems Brake by wire systems can be classified in three main categories, based on the type of actuation system, as stated below: 1. 2. 3.
Electro-hydraulic (EH) BBW systems Electromechanical (EM) BBW systems Electromagnetic (EMg) BBW systems (Regenerative braking via electric generator or Eddy Current braking)
The electro-hydraulic brake by wire system is composed of an electronically controlled pump/ motor assembly, a set electronically controlled valves, pressure transducers, brake pedal sensors, driver modules, and microcontrollers. Figure 8a illustrates the actuation system of an EH brake by wire system. The brake pedal sensor information is sent to microcontroller which interprets this in terms of a desired wheel cylinder pressure command. The microcontroller also receives the wheel cylinder pressure information from the pressure transducer. Based on the desired and actual wheel cylinder pressure values, the
microcontroller calculates appropriate pump and valve commands (via a control algorithm) which are then used to either build or dump the pressure in order to minimize the pressure error. The electromechanical (EM) brake by wire system is a dry system and no hydraulic fluid is used. It still uses the friction pads or shoes for braking the vehicle as in EH BBW system. However, the actuation mechanism is based on an electric motor and a mechanism that applies force onto the friction pads. Figure 8b illustrates the operation of this type of brake system (Hoseinnezhad, 2006). In addition to the brake pedal sensor, a number of other sensory information becomes necessary for such a system (e.g. clamp force, actuator position). The third type of brake by wire system is based on electromagnetic retardation. The principles of electromagnetic retardation are utilized to generate the braking forces which are contactless. As a result, no friction pad is needed in electromagnetic braking. Two principal types of actuators are used: (1) Eddy current brakes, and (2) Regenerative braking via generator.
19
Drive by Wire Systems
Figure 8a. Schematic of an electro-hydraulic brake by wire actuation system
Figure 8b. Electromechanical brake by wire actuation system
20
Drive by Wire Systems
Figure 8c. Electromagnetic brake by wire actuation system
Figure 8c shows a picture of an eddy current braking actuator which is composed of a wire wound stator and solid iron rotor. When the stator is energized, an electromagnetic field is generated which gives rise to Lorentz forces according to Maxwell’s equation when the rotor turns within the electromagnetic field. These forces oppose the motion of the rotor, thereby producing retarding torque. Regenerative braking works in a similar fashion. One limitation of electromagnetic braking systems is their dependency on the rotational speed, that is, the retarding torque is a function of the rotor speed. As a result, at lower speed, the retarding torque diminishes significantly and will not be able to provide sufficient braking torque. For this reason, electromagnetic brake by wire systems are rarely used as a standalone brake system. They are often used in conjunction with a friction brake system.
Steer B y Wire (SBW) Systems Steer by wire systems are in the early stage of development. Major automakers are yet to decide if and when such system will be incorporated in a vehicle. Similar to the brake by wire systems,
SBW systems use microcontrollers to actuate the road wheel in the lateral direction based on the hand wheel steering angle and vehicle speed information. Figure 9 illustrates a possible configuration of a steer by wire system. In this particular configuration, the hand wheel steering angle is measured via an appropriate sensor which is then communicated to two microcontrollers. These microcontrollers also receive vehicle speed information from the vehicle communication bus. Based on the steering wheel angle and vehicle speed information, the microcontrollers calculated the road wheel angle command via suitable algorithm. The microcontrollers then tries to maintain the commanded road wheel angle by actuating the rack mounted motors in a closed loop fashion via road wheel angle feedback. Steer-By-Wire systems are a relatively new development compared to the traditional mechanical, hydraulic or electric steering systems that are currently used for motor vehicles. In the SBW system, there is no mechanical coupling between the steering wheel and the steering mechanism, i.e. the vehicle’s steering wheel is detached from the steering mechanism during normal operation. Even though the mechanical
21
Drive by Wire Systems
Figure 9. Schematic of an SBW system
linkage between the steering wheel and the road wheels has been eliminated, a SBW steering system is expected not only to implement the same functions as a conventional mechanically linked steering system, but also to provide the advanced steering functions. There are a number of important steering functional requirements for a Steer-By-Wire system: • • • • •
Directional control and wheel synchronization. Adjustable variable steering feel. Adjustable steering wheel returns capability. Variable end of travel stop for steering wheel. Variable steering ratio.
It can be noticed that all of the drive by wire systems described above comprised of multiple redundancy in the sensors, actuator, microcontrollers, and communications buses. This is obviously by design which ensures a fault tolerant operation. Of the three drive by wire systems presented above, both BBW and SBW systems are considered safety critical systems which
22
means that any failure of the system may lead to catastrophic consequences. As a result, these systems must be designed to withstand multiple faults or failures within the system, before reaching conditions of catastrophic proportions. A fault in sensor, actuator, or microcontroller must not cause any severe consequences. This fault tolerance feature requires multiple redundancy in the hardware architecture which provides fault tolerant operation.
PERFORMANCE ENHANCEMENTS AND ACTIVE SAFETY VIA DRIVE BY WIRE SYSTEMS Drive by wire systems can significantly enhance the design and performance of the vehicle active safety systems such as the collision avoidance systems, adaptive cruise control, active front steering, stability control, road condition warning. Figure 10a-b illustrates trend of incorporation of various active safety systems in automobiles and driver error mitigation via DBW systems. The more advanced systems (e.g. platooning, highway copilot, autonomous driving) can only
Drive by Wire Systems
Figure 10a. Trend of active safety systems in automobiles
Safety Potential
Max
Underfloor Concept Side Airbag Side Impact Protection Airbag Deformation Elements Compound Glass Seat Safety Belt Cell
Low
ETC ABS
Autonomous Driving
Highway Copilot Platooning Emergency Brake Environment recognition Road recognition (LDW) Autonomous Cruise Control ASRESP
Passive Safety (reducing personal injury in case of an accident)
Active Safety (avoiding an accident)
Modified Source : Auto-Zeitung Nr. Jan.9
960
90 980
990
000
.....
Figure 10b. Driver errors and possible solution via DBW systems Error
Automated solution
Get into wrong lane
Navigation system
Forget which gear
Automatic gear shift
Only half an eye on the road
Fully automated driving system
Distracted, need to brake hard
Anti-lock braking system
Plan route badly
Route planning and navigation system
Fail to recollect recent road
Navigation system
Wrong exit from roundabout
Navigation system
Intended lights, switched wipers
Daylight sensor and automatic lights
Forget light on main beam
Automatic lighting
Usual route taken by mistake
Navigation system
Misjudge speed of oncoming vehicle
Collision avoidance system
Queing, nearly hit car in front
Collision avoidance system
Driving too fast on dipped lights
Vision enhancement system
Turn left into car's path
Collision avoidance system
Miss motorway exit
Navigation system
Manouver without checking mirror
Collision avoidance system
Fail to see pedestrian crossing
Collision avoidance system
Brake too quickly
Anti-lock braking system
Hit something when reversing
Collision avoidance system
Overtake without using mirror
Collision avoidance system
Misjudge gap in car park
Collision avoidance system
Turning left, nearly hit road user
Collision avoidance system
Misjudge interval turning right
Collision avoidance system
Try to pass vehicle turning right
Collision avoidance system
Fail to see pedestrian stepping out
Collision avoidance system
Attempt to drive off in third gear
Automatic gear shift
Try to drive off without starting car
Automatic start-up
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Drive by Wire Systems
Figure 11. Possible active safety features via the DBW system in an automobile
be effectively achieved via drive by wire system. Other active safety systems are a natural fit for drive by wire systems as these systems can easily be incorporated via the addition of a software module in a drive by wire equipped vehicle. As described in (Anwar, 2006), the ABS algorithm was implemented via only software update to a hybrid brake by wire system. In addition to passenger vehicles, SBW systems have also been investigated in the context of large construction machineries such as wheel loaders with superior performance enhancements (Haggag et al, 2005). Drive by wire systems can also significantly enhance the performance of a vehicle by taking proactive actions in a driving scenario. For example, vehicle handling can be made more responsive by adding slight oversteer on high friction coefficient surface which can easily be done via a steer by wire system (Hebden et al, 2004; Yih & Gerdes, 2005; Chang, 2007). Performance enhancement via SBW system while maintaining stability has also been investigated by a number of researchers (Limpibunterng & Fujioka, 2002; Oh et al, 2004; Segawa et al, 2004; Limpibunterng & Fujioka, 2004). Similarly vehicle suspension can be tuned dynamically via active suspension control system to adapt to road conditions the vehicle is in. Figure 11 illustrates possible scenarios where drive by wire systems are utilized to improve overall vehicle safety via actively monitoring vehicle states and taking corrective actions or warning the driver. 24
Both brake-based Yaw Stability Control (YSC) and Anti-lock Brake System (ABS) have been demonstrated on BBW systems (Anwar, 2005; Anwar, 2006). Using sliding mode control (SMC) theory, the author provided experimental results for the hybrid BBW system. Here it was emphasized that the ABS system was implemented only through the software and no hardware modification was needed. YSC algorithm via active front wheel steering has also been developed and tested in a SBW equipped vehicle (Yih & Gerdes, 2005; Zheng & Anwar, 2008). The test results from these studies indicate that the steering based YSC system on a SBW equipped vehicles has significant safety advantages in critical situations over a conventional vehicle where the driver has to manually control the vehicle against an unexpected yaw motion.
TECHNOLOGICAL CHALLENGES FOR DRIVE BY WIRE SYSTEMS AND POSSIBLE SOLUTIONS The explosion of the usage of the electronic control systems is nowhere more apparent than in the modern automobiles. During the last two decades, advances in electronics have revolutionized many aspects of automotive engineering, especially in the areas of engine combustion management and vehicle safety systems such as anti-lock brakes
Drive by Wire Systems
(ABS) and yaw stability control (YSC). The benefits of applying electronic technology are clear: improved performance, safety and reliability with reduced manufacturing and operational costs. However, only recently has the electronic revolution begun to find its way into automotive chassis systems in the form of electronically controlled variable assist and, within the past few years, fully electric power assist. The complexity of the DBW systems arises from the fact that these systems must incorporate multiple redundant sensors, actuators, controllers, and communications networks to achieve fault tolerance. The fault tolerant control of these systems is accomplished via appropriate fault detection, isolation, and accommodation (FDIA) algorithms. However, the total number of redundant components makes the DBW systems prohibitively expensive. Also, in order to accurately detect and isolate a component failure without raising any false alarms, fast and robust detection algorithms are needed. A number of issues must be resolved before drive by wire system equipped vehicles can be brought to the market. Cost and reliability are two major challenges facing these systems. While reliability of these systems can be improved via increased redundancy and fault tolerant control algorithms and communication protocols, the physical redundancy of components makes these systems prohibitively expensive. In this section, we highlight the major challenges facing the drive by wire systems including cost, reliability, packaging, control, and communication protocols. The numerous benefits of DBW systems outweigh the risks in introducing such systems in the future automobiles. The concept vehicles with DBW systems have demonstrated such benefits (Stanton & Marsden, 1997). However, the overall cost of highly reliable DBW system is still several times higher than the conventional systems; primarily due to the presence of multitude of redundant components (sensors, microcontrollers, actuators etc.). Model-based fault detection tech-
niques can be one possible solution, which could lower the overall cost without compromising reliability.
Fault Tolerant Control Fault tolerance is a property of a system that continues operating properly in the event of failure of some of its parts. It provides the ability of a system to provide a service complying with the specification in spite of faults. If operating quality decreases at all, the decrease is proportional to the severity of the failure, as compared to a naively-designed system in which even a small failure can cause total breakdown. In order to bring down the cost, the total number of redundant components must be reduced without compromising the fault tolerance. One possible solution to this problem is to utilize analytical redundancy or model based fault detection, isolation, and accommodation. Model-based Fault Detection and Isolation (FDI) explicitly use a mathematical model of the system. It is motivated by the conviction that utilizing deeper knowledge of the system results in more reliable diagnostic decisions. The main idea is “analytical redundancy” which makes comparison of measurement data with known mathematical model of the physical process. It is superior to “hardware redundancy” generated by installing multiple sensors for the same measured variable. They offer simplicity, flexibility in the structure, less hardware, less weight, and cost. Model-based Fault Detection and Isolation is achieved by implementing a more complex failure detection algorithm that takes careful account of system dynamics, which may be able to reduce requirements for costly hardware redundancy. Analytical redundancy based FDI uses a model of the dynamic system to generate the redundancy required for failure detection. In many systems, all of the states cannot be measured because of cost, weight and size considerations, therefore, FDI schemes for such systems must extract the
25
Drive by Wire Systems
redundant information from dissimilar sensors, using the differential equations that relate their outputs. In addition to taking hardware issues into consideration, the designer should consider the issue of computational complexity. Most model-based FDI methods rely on analytical redundancy. In contrast to physical redundancy, when measurements from different sensors are compared, now sensory measurements are compared to analytically obtained values of the respective variable and the resulting differences are called residuals. The deviation of residuals from the ideal value of zero is the combined result of noise, modeling errors and faults. A logical pattern is generated showing which residuals can be considered normal and which ones indicate a fault. Such a pattern is called the signature of the fault. The final step of the procedure is the analysis of the logical patterns obtained from the residuals, with the aim of isolating the failures that cause them. Such analysis may be performed by comparison to a set of patterns known to belong to sample failures or by the use of some more complex logical procedure. One major difference between pure DBW systems and other electronically controlled vehicle systems is that even if these control systems fail, the basic functionality of the brake and steering system remains intact. In case of DBW systems, a component failure can result in loss of function-
ality unless the DBW system has redundancy in its components by design. A DBW system with mechanical backup will allow a “limp home” mode in case of a component or subsystem failure. However, the mechanical backup is tantamount to having the basic mechanical system (e.g. steering or braking) and hence cannot be justified from a cost standpoint. Following the example of fly by wire systems, it is only natural to focus on drive by wire system without mechanical backup. However, DBW systems are microprocessor-based control system based on sensor inputs and electronically controlled actuation systems (via communication bus) which call for fault tolerance in case of component failure. Figure 12 shows an example of the redundancy architecture of a fault tolerant drive by wire system. Safety integrity is defined by the probability of a safety-related system satisfactorily performing the required safety functions under all the stated conditions within a stated period of time. Safety and reliability of a DBW system can generally be achieved by a combination of the following: fault avoidance, fault removal, fault tolerance, fault detection and diagnosis, automatic supervision and protection. Fault avoidance and removal has to be accomplished mainly during the design and testing phase. Analytical methods to evaluate the effects of faults on system reliability and safety exist: reliability
Figure 12. Redundancy architecture of a drive by wire system
26
Drive by Wire Systems
analysis, fault tree analysis (FTA), failure modes and effects analysis (FMEA), hazard analysis (HA), and risk classification. Certain component faults / failures can occur even after designing the hardware based on reliability and safety analysis. These faults must be tolerated, generally, by adding redundancy in the components, units, or subsystems. Redundancies in sensors, actuators, microprocessors, communication buses, power supplies are considered to achieve the fault tolerance. Two basic approached to fault tolerance: static redundancy and dynamic redundancy. Static redundancy uses three or more parallel modules that have the same input signal and the fault in any one module is detected by two-outof-three voting (Figure 13a). A single fault can such be tolerated by a triple redundant module. The maximum number of failures that can be tolerated by an n-redundant system via static redundancy is (n-1)/2 (n being odd).
Dynamic redundancy requires fewer modules at the cost of more information processing (Figure 13b). In dynamic redundancy, at least two redundant components are required where one component works in default mode while the other works as a standby and takes over the main components if it fails. The fault in the main component is determined by consistency checking of its output signal (such as range of signal and its rate of change) and comparing with that from the standby component. For microprocessor, parity checking or watchdog timers may be used to detect the faulty microprocessor. After a fault is detected, a reconfiguration module then switches the standby component to take over and removes the faulty component from operation. For static redundancy, at least three redundant component output signals are required for accurate fault detection via majority voting algorithm. In case of dynamic redundancy, model-based
Figure 13. Static and dynamics redundancy architectures
27
Drive by Wire Systems
Figure 14. Fault detection flow diagram
approaches can be utilized to detect fault since output signals are readily available from various components. Static redundancy can be found in various mechanical and electrical systems: spoke-wheel, multiple brushes in DC motor. Only single point hazardous failures are considered for mitigation upon fault detection. It has been shown that consideration of only single point failures is sufficient for a DBW system as opposed to a Fly By Wire (FBW) system. Fault detection is an integral part of a fault tolerant control system. Fault detection methods based on measured signals can be classified as follows: • • •
28
Limit value checking (thresholds) and plausibility checks ranges of single signals. Signal-model-based methods for single periodic or stochastic signals. Process-model-based methods for two or more related signals.
In order to obtain specific symptoms, it is necessary to have more than one input and one output signal for parity equations or output observers. For parameter estimation, it is necessary to have at least one input signal and one output signal. Figure 14 illustrates the fault detection process that utilizes both signal model based fault detection and process model based fault detection. Comprehensive overall fault-tolerance design can obtain fault tolerant components and fault tolerant control. This, in turn, can be achieved via fault tolerant lower cost components with built-in fault tolerance. A fault tolerant sensor configuration should be at least fail-operational after one fault. This can be obtained via hardware-redundancy with same type of sensor or by analytical redundancy with difference sensors and process models. Fault tolerant actuators can be designed by using multiple actuators in parallel, with either static or dynamic redundancy with cold or hot standby,
Drive by Wire Systems
e.g. static redundancy in hydraulic actuators in a fly-by-wire system. Fault tolerant actuators can also be designed with redundancy in actuator components that have the lowest reliability, e.g. two servo valves for hydraulic actuators or three windings on an electric motor are examples of static redundancy. The cabin pressure flap actuator in an aircraft uses dynamic redundancy with cold standby via two independent DC motors acting on a single planetary gear.
Fault Tolerant Communication In-car electronics plays an important role in many automotive applications: Throttle-by-wire, Steer-by-wire, Brake-by-wire, and many other systems. The number of electronic components in a car has therefore significantly increased. In particular, in-car networks used to interconnect electronics equipments becomes a key point. A common practice in the past was the extensive use of wiring. These wires significantly increased the complexity and weight of these systems. Today, in-car bus networks are largely adopted for the sake of reducing the vehicle weight and fuel consumption. The requirements of an automotive communication network are generated from the applications it has to support. Major automotive applications that involve networking and the usage of field buses are: • • • •
Drive by wire Chassis systems Powertrain systems Infotainment or multimedia
Multimedia devices require high bandwidth and plug-n-play capabilities. Two most important requirements for DBW systems are dependability and fault-containment. Distributed control is often used to meet these requirements. Distributed control algorithms require a coordinated snapshot of the controlled object. Such coordination can be achieved by the provision of a global time base.
Figure 15. Common field bus technologies between microprocessors
Unification of bus systems within vehicles: Controller Area Network (CAN) will remain the selection of choice for event-triggered systems while a new protocol is needed for applications which require high performance, determinism, fault-tolerance, and flexibility. Several different field buses are used to address these various communication demands. A big issue that the automotive system producers deal with is that too many field bus technologies exist today. To interconnect these systems there is a need for high bandwidth together with flexibility and determinism. It is desirable to move from using too many technologies to fewer more general. To reduce the complexity evolving in a modern automotive system, it would be good to commit on a set of networking protocols that can be used in most of the applications typically. The common field bus technologies interconnecting Electronic Control Units or ECUs today are: Controller Area Network (CAN), Local Interconnect Network (LIN), Byteflight, and Media Oriented Systems Transport (MOST). Figure 15 shows a graphical illustration of the requirements for various communication protocols.
Analytical Redundancy Based Fault Tolerant Control of DBW Systems Physical redundancy based fault tolerant control and communication protocols can significantly enhance the reliability. However, this method-
29
Drive by Wire Systems
ology increases the overall system cost exponentially due to the presence of a large number of redundant hardware components. One possible solution to this problem is to replace the physical redundancy of the fault tolerant drive by wire systems by analytical redundancy. Analytical redundancy methodologies can not only reduce the overall system cost by reducing the total number of redundant components, but also further improve overall reliability of the system through the usage of a diverse array of sensory information. An up to date in-depth coverage of analytical redundancy based fault tolerant control and communication methodologies will be given in this section including predictive and nonlinear methodologies. The concept of analytical redundancy has been investigated in the context of aerospace applications, primarily utilizing Eigen-structure theory. However, most of these articles were aimed at isolated subsystems in an aircraft or a spacecraft. Fly-By-Wire (FBW) systems are mostly based on full hardware redundancies (Bajpai et al, 2001). As a result, analytical redundancy methodologies have not been utilized to a great extent in FBW systems. Nonetheless, the theoretical foundation for analytical redundancy methodology researched in aerospace applications can be useful in DBW system research. Introduction of Drive-By-Wire technology is more challenging in the automobile market, mainly because automobile consumers cannot afford the high cost of redundant systems the aerospace industry can. Each extra sensor, actuator, and Electronic Control Unit (ECU) increases the overall cost and weight of the vehicle. With the profit margin already low, this approach will not be acceptable to the automobile industries. By employing analytical redundancy techniques instead of hardware redundancy, it will be possible to bring the overall cost of such system down to a point that will be attractive to the automakers for mass production without sacrificing the high level of safety and reliability required by the consumers. Analytical redundancy along
30
with appropriate fault detection, isolation, and accommodation methodologies can be utilized to make the DBW systems fault tolerant (Gadda et al, 2007). Through analytical redundancy, the vehicle steering angle can be estimated from the states of other vehicle parameters without using an extra steering position sensor.
Sn ANALnalytical REDUNDANCY in a STEER BY WIRir In this research, the analytical redundancy based fast fault detection algorithms was developed that based on physical models, nonlinear estimator and generalized predictive algorithms. In this algorithm, outputs from a number of redundant sensors as well as analytical sensor are checked against each other for a number of times before declaring a component to be faulty. The analytical sensor output is the combination exertion of the full vehicle model, nonlinear Sliding Mode Observer, and the long range prediction algorithm. The yaw rate signal can be measured with inexpensive sensors. Therefore with the measured yaw rate and the measured motor current input, the road wheel steering angles are estimated with the Sliding Mode Observer. Thereafter the steering angles were predicted by a long range predictor at variable prediction horizons with the participation of estimated steer angle and the motor current. The proposed research concept is utilizes the long range prediction based fault detection, based on the analytical model of the SBW system (Figure 16) which would provide added safety to the SBW system via fast and robust fault detection and isolation of a component failure in such a system. Since analytical redundancy methods are model-based, long-range prediction based Fault Detection, Isolation, and Accommodation (FDIA) algorithms are appropriate in such an
Drive by Wire Systems
Figure 16. Electronic architecture of an SBW system
application since modeling errors are inevitable in real-world systems. Furthermore, long-range prediction based FDIA methods (Figure 17) provide robustness against external disturbances which are expected in a DBW vehicle having a multitude of electric and electronics components. The fundamental concept in this proposition is that the sensor outputs are compared against the analytical counterpart (analytical redundancy) whose outputs are predicted several time step ahead via generalized predictive algorithm. In the event of a component failure, the predicted output will deviate from the sensor outputs several time steps ahead, thus reducing the detection latency. Table 1 lists the notations used in this section.
Observer and Predictor Based Modeling
Table 1. Notations θ
Road wheel angle
β
Vehicle body side slip angle
r
Vehicle yaw rate
a
Distance from the front tire to the vehicle’s CoG
b
Distance from the rear tire to the vehicle’s CoG
m
Vehicle mass
V
Vehicle longitudinal velocity
Iz
Vehicle moment of inertia
Jw
Moment of inertia of the road wheel
bw
Viscous damping coefficient for wheel bearing
C α,f and C α,r
Front and rear tire cornering coefficients
km
Motor torque constant
tp
Pneumatic trail
tm
Mechanical trail
τf
Friction torque at the road wheel
im
Motor current
Ej and Fj
Uniquely defined polynomials in the Diophantine equation
An observer can be designed by combining the steering system model and vehicle model (Hasan & Anwar, 2008): x = Ax + Bim + E τ f T x = β r θ θ Cα , r b − Cα , f a −Cα , f − Cα , r −1 + … mV mV 2 … 2 … Cα , r b − Cα , f a −Cα , f a − Cα , r b 2 … Iz I zV A=… … 0 0 … a (t p + tm )Cα , f …(t p + tm )Cα , f … Jw J wV T
Cα , f mV Cα , f a Iz 0 −(t p + tm )Cα , f
km 1 B=… 0 0 0 0 0 0 − ⁄ ⁄ ;E = … Jw Jw C = [0 0 1 0]
Jw
0 ⁄ ⁄ ⁄ 0 ⁄ ⁄ 1 ⁄ ⁄ −bw ⁄ ⁄ jw
T
(4.1)
The motor current is the input to the system and the torque due to Coulomb friction is treated as a disturbance. The above system is fully observable.
Sliding Mode Observer (SMO) The motivations for using Sliding Mode Observer are, it is model free and robust respect to bounded
31
Drive by Wire Systems
Figure 17. Road wheel angle estimation and fault detection, isolation, and accommodation (FDIA)
θ des
+
θ
−
im
θ1
θ2
θ predicted θ estimated
r
The order of the observer may be reduced due to the fact that Rank (C) = l and the observed vector may be represented as:
θ FDIA
uncertainty. It can work under much less conservative condition. The idea underlying SMO observer design methods can be illustrated for a linear time-invariant system (Hasan & Anwar, 2008): x = Ax + Bu y = Cx y ≠ Rl, x ≠ Rn, rank(C) = l
(4.2)
The pair (C, A) is assumed to be observable and n is order of the system. A linear asymptotic observer is designed in the same form as the original system (4.2) with an additional input depending on the mismatch between the real values and the estimated values of the output vector: xˆ = Axˆ + Bu + L( y − Cxˆ )
(4.3)
where xˆ is an estimate of the system state vector and L ≠ R n×l is an input matrix. The state vector of the observer xˆ is available since the auxiliary dynamic system is implemented in a controller. The motion equation with respect to mismatch x = x – xˆ is of form: x = ( A + LC ) x
32
The behavior of the mismatch governed by homogeneous Equation (4.4) is determined by eigenvalues of matrix (A+LC). For observable systems, they may be assigned arbitrarily by a proper choice of input matrix, L. It means that any desired rate of convergence of the mismatch to zero or estimate xˆ (t) to state vector x(t) may be provided. Then any full-state control algorithms with vector xˆ (t) are applicable.
(4.4)
y = C1x1 + C2 xp x = [x1 xp]T x1 ≠ R l, xp ≠ R n–l, det(C1) ≠ 0
(4.5)
It is sufficient to design an observer only for vector xp, then the components of vector x1 are calculated as: x1 = C1–1 (y – C2 xp)
(4.6)
Write the system Equation (4.2) in space (y, xp) as: y = A11 y + A12 x p + B1u x p = A21 y + A22 x p + B2u
(4.7)
where A MAM −1 = …11 A21
A12 C B , MB = …1 ⁄ , M = …1 A22 ⁄ B 2 0
C2 I n −1 ⁄
The coordinate transformation M in nonsingular, det(M) ≠ 0. Therefore, applying the simple Sliding Mode Observer (SMO) in the state space system equation, xˆ = Axˆ + Bu + L sgn( y − Cxˆ )
(4.8)
Drive by Wire Systems
where, sgn(z) = col(sgn(z1), ..., sgn(zn)
Generalized Prediction Bsed Predictor
+1 if , z > 0 sgn( z ) = −1 if , z < 0 Under a suitable choice of the gain matrix L in the observer, sliding occurs on the manifold y – C xˆ = 0, and it becomes equivalent to the reduced order observer. The discontinuous vector function v = L sgn(y – ŷ). Now from Equation (4.7), yˆ = A11 yˆ + A12 xˆ p + B1u + L1 sgn( y − yˆ ) xˆ p = A21 yˆ + A22 xˆ p + B2u + L2 sgn( y − yˆ ) (4.9) The system for the error y = y – ŷ is of the form, y = A11 y + A12 x p + B1u + L1 sgn( y ) x p = A21 y + A22 x p + B2u + L2 sgn( y )
(4.10)
The vector function v ≠ R l is chosen such that sliding mode is enforced in the manifold y = 0 and the mismatch between the output vector y and its estimate ŷ reduced to zero. A vector L2 must be found such that the mismatch x p = xp – xˆp between xp and its estimate xˆp decays as the desired rate. Equivalent value of the discontinuous function: L1 sgn(y) = A12 x p
(4.11)
For simplicity, L1 is considered as 1 and equation (4.11) becomes: sgn(y) = A12 x p
(4.12)
Now the equation on the sliding manifold appears from equation (4.10): x p = ( A22 − L2 A12 ) x p
(4.13)
A class of predictive self-tuning controllers, known as generalized predictive controller (GPC) (Hasan and Anwar, 2008) have shown robustness against unstable plants, non-minimumphase plants, model over parameterization, and uncertain process dead time. These controllers have also been observed to provide offset free behavior for the closed loop system since they include an integral action. These set of controllers have been very successful in regulator or tracking type observer applications. In the context of long range prediction, the prediction horizon, j is a tunable design variable that can be set to any value according to the desired prediction range. The predictive nature of the GP (Generalized Prediction) based predictor algorithm comes from the use of the Diophantine equation. Through the use of Diophantine equation, the output of the plant is predicted j-step ahead of present time. This prediction output is then used for future fault detection and identification. Considering the state space equation (4.2), a transfer function can be obtained as follows: G (s) =
U (s) = C ( sI − A) −1 B Y (s)
(4.14)
After discretization of the equation (4.14), a more general form of the dynamic model of the dynamic model of the vehicle model can be written as: A(z–1) Y(z) = B(z–1) U(z)
(4.15)
where A(z-1) and B(z-1) are polynomial of order na and nb respectively in the backward shift operator in time, z-1. A(z-1) and B(z-1) have the following forms:
33
Drive by Wire Systems
A( z −1 ) = d 0 + d1 z −1 + d 2 z −2 + ... + d na z − na B ( z −1 ) = n0 + n1 z −1 + n2 z −2 + ... + nnb z − nb (4.16) In order to make a prediction of the future output of the road wheel angle, the Diophantine identity is used to derive the j-step ahead prediction of ∆U(t+j). 1 = Ej(z–1) A(z–1) + z–j Fj(z–1)
(4.17)
Where Ej and Fj are uniquely defined polynomials for a given A(z-1) and the prediction interval j. In the present work, the recursive technique has been used to obtain Ej and Fj (Hasan & Anwar, 2008). This makes the procedure computationally very efficient. It has been shown that with increasing j only the highest order term in Ej+1(z-1) changes while the rest of the coefficients remain the same in Ej(z-1). Therefore, we can write: Ej+1(z–1) = Ej(z–1) + ej z–j
(4.18)
where, Ej(z–1) = e0 + e1z–1 + e2z–2 + ... + ej–1z–( j–1) In the degree of polynomial A(z-1) is na, then the degree of Fj(z-1) becomes na. The coefficients of the polynomial Fj(z-1) may then be denoted as: Fj(z–1) = f j,0 + f j,1z–1 + f j,2z–2 + ... + f j,naz–na (4.19)
Steering Angle Prediction GP based prediction supposed to be executed for the discrete model. Hence the forth order vehicle model has been discretized to make itself compliant with the predictor. From the equations (4.15)-(4.16), the Diophantine prediction equation ( j-step ahead predictor) is given by, Ej(z–1)(d0 + d1z–1 + d2z–2 + d3z–3 + d4z–4) ∆ + z–j Fj(z–1) = 1
34
(4.20)
Multiplying equation (4.20) with θ(t+j) and rearranging that equation, we obtain: θ(t + j) = Fj(z–1)θ(t) + Ej(z–1)(n0 + n1z–1 + n2z–2 + n3z–3 + n4z–4)∆im(t – j + 1) (4.21) where, ∆ = (1 – z–1) Equation (4.21) predicts the value of the pinion angle θ in the future ( j - time step ahead). θ(t + j) = F × θ(t) + EB × ∆im(t – j + 1) (4.22) The matrices F ≠ R N×5 and EB ≠ R N × (N + 5)are calculated by using the MATLAB script.
Simulation RESULTS Initialization of Vehicle Model for Simulation In the full vehicle model, the parameter initialization (Anwar & Chen, 2007) is set as follows: Fw = 2 (N-m); Jw = 0.5 (kg-m2); bw = 20; k m = 0.5; I = 3136 (kg-m2); Cf = 18000 (N/rad); Cr = 47000 (N/rad); m = 1250 kg; V = 8 (m/s); a = 1.05 (m); b = 1.71 (m); C0 = (Cf + Cr) (N/rad); C1 = (bCr – aCf) (N-m/rad); C2 = (a2Cf + b2Cr) (N-m2/rad); tp = 0.0381 (m); tm = 0.04572 (m); C3 = (tp + tm)Cf (N-m/rad); K = 80; T = 0.005 (sec). The vehicle model presented in this thesis is evaluated on a validated SIMULINK model (Anwar & Chen, 2007). The cornering coefficients have been considered for a light weight passenger Front Wheel Drive (FWD) car. Therefore the mass and dimensions of the vehicle were perceptibly standard for the small passenger car. The simulation process has been appraised for a slow moving vehicle and the pneumatic and mechanical trails have been carried out from the standard passenger car tires specification. The driver’s factor is chosen as nominal driving effect. But the sample time can be deviated according to the dynamic requirement of the system.
Drive by Wire Systems
stage of the system operation otherwise according to their nature; they are gradually increased to a larger extent that could be difficult to control. The amplitude change type incipient faults are shown in Figures 19a and 20a. These two most available types faults are introduced into the vehicle model system to verify the FDIA methodology as well as provide the efficient proposition of SMO and GP based predictor for the SBW system. In order for the SBW system to be robust, the sensor measurements must be accurate and reli-
Faults Types and Their Implications Two major fault types have been introduced into a steer by wire system, namely: • •
Permanent fault Incipient fault
The persistent fault type is illustrated in Figure 18a. Amplitude change fault can be either positive amplitude change or negative amplitude change types. Incipient fault should be handled at the early
Figure 18a. Persistent zero sensor fault introduced in one of the two physical sensors with the fault state displayed on the right 30
Analytical Sensor Physical Sensor-1 Physical Sensor-2
Steering Angle (Degree)
20
10
0
-10
-20
-30
0
2
4
8
6
10
Time (Sec)
12
14
20
18
16
Figure 18b. FDIA output of sensor data after removing the persistent zero introduce fault in one physical sensor 25
Desired Road Wheel Angle FDIA Output
20
Steering Angle (Degree)
15 10 5 0 -5 -10 -15 -20 -25
0
2
4
6
8
10
Time (Sec)
12
14
16
18
20
35
Drive by Wire Systems
able. Therefore, any faulty signal must be eliminated to prevent undesirable steering effects. The Fault Detection, Isolation, and Accommodation algorithm (FDIA) (Anwar & Chen, 2007) used in this paper is able to handle single point faults without interrupting the functionality of the SBW system. This algorithm can be easily modified to handle multiple faults if more than three-sensor signals are compared. The FDIA algorithm implemented in SIMULINK below is based on a majority voting scheme in which a minimum
of three signals are required for this scheme to work. The sensor signals are compared against each other in real-time to determine the faulty signal where majority is assumed to be correct. This is based on the assumption that the event of a sensor failure is rare and the event of multiple simultaneous sensor failures is extremely rare. This algorithm can determine which sensor has failed by comparing its value against other sensors’ values. This algorithm can manage hard-failures as well as soft-failures. Hard-failure is characterized
Figure 19a. Negative amplitude change type incipient fault introduced in one of the two physical sensors with the fault state displayed on the right 30
Analytical Sensor Physical Sensor-1 Physical Sensor-2
Steering Angle (Degree)
20
10
0
-10
-20
-30
0
2
8
6
4
10
Time (Sec)
12
14
20
18
16
Figure 19b. FDIA output of sensor data after removing the negative amplitude change type incipient fault in one physical sensor
25
Desired Road Wheel Angle FDIA Output
20
Steering Angle (Degree)
15 10 5 0 -5 -10 -15 -20 -25
36
0
2
4
6
8
10
Time (Sec)
12
14
16
18
20
Drive by Wire Systems
Figure 20a. Positive amplitude change type incipient fault introduced in one of the two physical sensors with the fault state displayed on the right 50
Analytical Sensor Physical Sensor-1 Physical Sensor-2
40
Steering Angle (Degree)
30 20 10 0 -10 -20 -30 -40 -50
0
2
4
6
8
10
Time (Sec)
12
14
16
18
20
Figure 20b. FDIA output of sensor data after removing the positive amplitude change type incipient fault in one physical sensor 30
Desired Road Wheel Angle FDIA Output
Steering Angle (Degree)
20
10
0
-10
-20
-30
0
2
4
6
8
by an abrupt or sudden sensor failure and softfailure is characterized by biases or drifts in the signal over time. When a sensor fails, its signal is no longer used in the road wheel angle calculation. In such a situation the driver would be alerted of the sensor failure, but would also still be able to maintain safe control of the vehicle. A number of simulation runs were performed in order to evaluate the developed methodology of estimating and then predicting the road wheel
10
Time (Sec)
12
14
16
18
20
angle to detect and control them. And some simulations were observed to verify the advantages of higher prediction horizon into the dynamic systems. The SBW controller, the yaw angle observer, the road wheel angle estimator, the FDIA algorithms, and the Generalized Predictive (GP) based predictor are combined with a simplified vehicle model with an SBW actuation system. The combined model was given a sinusoidal steering input. Fault was then injected to one of the three road wheel angle sensors whether one of them
37
Drive by Wire Systems
Figure 21a. Effect of prediction horizon for the amplitude change type incipient fault (lower magnitude) into the physical sensor
Fault detection time by FDIA (Sec)
4.5
4.48
4.46
4.44
4.42
4.4
4.38
0
1
2
3
4
5
6
Prediction Horizon, j
7
8
9
10
Figure 21b. Effect of prediction horizon for the amplitude change type incipient fault (higher magnitude) into the physical sensor.
Fault detection time by FDIA (Sec)
23.25
23.2
23.15
23.1
0
1
2
3
was analytical sensor. Fault flags and the output of the road wheel angle from the FDIA block are then recorded. Persistent zero introduce fault was injected by making the sensor out a constant value. It was considered a faulty signal by the FDIA block and was eliminated from the FDIA block output. Figure 19b shows the FDIA block output with the removal of the fault from the system. Figures 19a and 20a show the sensor signals of the amplitude change type incipient faults. These faults are introduced into the 2nd physical
38
4
5
6
Prediction Horizon, j
7
8
9
10
sensor though m-file. These two figures show the nature of incipient fault as gradual increment with time. According to the proposed estimated and predicted fault tolerant control methodology, these faulty signals were eliminated and the FDIA output signals (Figures 19b and 20b) verify the efficiency of the GP based prediction. Now to show the effect of GP based prediction with the different prediction horizons, it is convenient to observer the effects with the incipient fault. Because by definition the incipient fault is gradually increases with time, the consequence
Drive by Wire Systems
of prediction horizon is more prominent. Figures 21a-b show the effect of prediction horizon for the various magnitudes of amplitude change type incipient fault. It illustrates that the fault detection time for incipient faults decreases significantly with increase in the prediction horizon, thereby improving the efficiency of fault detection. This research work has demonstrated that it is possible to increase the level of robustness of a fault tolerant SBW system via successful implementation of SMO and GP based fault tolerant control. In the present work, a nonlinear Sliding Mode Observer (SMO) was designed and implemented to estimate the road wheel angle with available sensor output of yaw angle and motor input current. Through the proposed predictive analytical redundancy based fault detection and isolation algorithm, an extra level of redundancy was possible without any extra hardware. The proposed algorithms rendered the SBW system with a robust fault tolerant system as evidenced by the simulation results. It was also observed that the reliability of the proposed methodology increases with the increase of prediction horizon as it reduces the detection time for the faulty sensors.
FUTURE TRENDS Various drive by wire systems in the same vehicle may share a diversity of sensors and actuators in order to bring down the overall system cost. This poses a unique challenge/opportunity for integration of all the sensory information via data fusion methodologies that will take the analytical redundancy features of these systems to a new level. In addition, integration of fault diagnosis and control bring about new challenges since the control objectives and diagnosis objectives may have conflicting features. For example, in an SBW system, the control objectives could be noise/disturbance rejection and command tracking whereas the diagnosis objective could be
faulty signal tracking and noise reduction (Zheng et al, 2006). Integrated control of various drive by wire systems (e.g. TBW, BBW, SBW) is also being investigated for future application on DBW vehicles (Abe & Mokhiamar, 2007; Sameshima et al, 2004; Lu & Hedrick, 2005; Setlur et al, 2006). Robust sliding mode control of a throttle body for drive by wire operation of automotive engines has been critical in the success of TBW systems (Rossi et al, 2000). Future communication bus for DBW systems are also being developed and implemented (Shaheen et al, 2003). We will focus on the future trend of this important DBW technology which is very critical to the success of these systems. Modern automotive system needs both timetriggered (e.g. FlexRay) and event-triggered (e.g. CAN) traffic. We analyze the relevant feature of these communication buses below. Controller Area Network (CAN): • Very good in handling event-triggered traffic • Can be found on many places in a car, ranging from engine control and ABS-systems to body and comfort systems • Has also been extended with a time-triggered session layer, as TT-CAN, to support time-triggered traffic May provide a smooth transition using CAN • in the first generation “wet” DBW where a hydraulic backup is used However, since it relies on the CAN lower • layers, it is lacking fault-tolerant mechanisms and bandwidth capabilities Time Triggered Protocol (TTP): • Highly fault-tolerant network • Intended for safety-critical systems such as DBW and avionics • Implemented fault tolerant mechanisms, such as atomic broadcast using membership service, distributed clock synchronization and bus guardians • Ensures that there can be no single point of failure 39
Drive by Wire Systems
Figure 22. Application of communication bus protocols in a drive by wire vehicle
• •
Very deterministic at the cost of being less flexible in terms of message transmissions Weakness: Inflexible message transmission
FlexRay: • Allows for both time-triggered and eventtriggered message transmissions • Developed with safety-critical applications in mind, just like TTP • Hence, using FlexRay it is possible to develop both DBW systems and powertrain systems, reducing the need for several field bus technologies Has the biggest potential for becoming the • next generation automotive network for safety-critical fault-tolerant applications, mainly because it is heavily backed up by industrial partners and pushed by most major industrial automakers
40
Although FlexRay is developed and mainly intended for safety-critical communications such as DBW, it is possible to use it in several other automotive applications. The possible solution is the implementation of higher-layer extensions on top of FlexRay, which will make it suitable for a broader class of automotive applications. The higher-layer extensions could be especially devised for better supporting dynamic traffic scenarios. Figure 22 illustrates an in-vehicle network architecture with FlexRay. As shown, other non-time triggered communication buses can be used for non-safety critical applications (e.g. door locks, climate control). Considering the most likely application domains, mixed configurations will be dominant. Minislotting offers FlexRay with dynamic segment capability. With FlexRay, the dynamic segment is scheduled in a cyclic manner using a minislotting technique similar to Byteflight. As the dynamic segment is started, the minislotting
Drive by Wire Systems
mechanism is used to schedule the messages. In order for the minislotting mechanism to work, all messages must have unique identifiers (IDs), like in CAN. Moreover, all nodes in the system keep a slot-counter. As a dynamic segment is started, all slot counters are reset to 0 indicating the start of the first dynamic slot. Whenever a node has a message with an ID matching the slot counter, the node will send its message. Once the message has been sent, then all the nodes in the system will detect a new dynamic slot and increase their slot counters by one. If there is no message transmitted within a short time D after the initiation of a dynamic slot, a new dynamic slot is detected and all the slot-counters are increased again, etc. In this way messages are scheduled in increasing order based on their IDs, serving the lower value IDs first. Hence, a low value ID gives a high priority to a message and thus a higher chance of being transmitted. A dynamic slot can be of varying size depending on the size of the message transmitted in the dynamic slot. However, the slot-counters might not reach their maximum value due to the fact that the dynamic segment does not fit all possible messages. As the dynamic segment is of fixed size, depending on the number of dynamic slots used and their individual lengths, the slot-counter will only reach
a certain value. Therefore some messages might have to wait one or more communication cycles in order to be scheduled. It should be noted here that the slot-counters always begin with value 0 for all dynamic segments. In summary, FlexRay offers high speed time triggered communication. One of the constraints of current automotive electronic communications systems is the limited bandwidth. For a start, the FlexRay Consortium selected a data transfer rate of 10 MBit/s on each of the two communication channels as a viable trade-off between the need for high performance and the economic constraints. As the current FlexRay specification is open for higher bit rates, faster physical layers are possible in the future. FlexRay communication bus can also be designed for fault-tolerance. Scalable system fault-tolerance can be achieved via the support of either single or dual channels. Since the needs of automotive applications differ quite substantially in terms of fault-tolerance, FlexRay can do the splits between distributed fault-tolerant and non fault-tolerant systems: •
Single-channel system (doubling the data throughput)
Figure 23. (a) Composability based on ECU nodes; (b) Composability of data from different channels
(a)
(b)
41
Drive by Wire Systems
Figure 24. Possible configuration of FlexRay bus architecture
• •
Dual-channel system (full redundancy) Dual-channel system with mixed connectivity (some nodes connect to both channels, some connect only to one)
For safety-critical systems, communication reliability cannot be based on probabilistic measures. A CAN system is not possible to calculate the communication delay except for some high priority message. FlexRay guarantees the transport for safety-critical message with known latency. FlexRay offers deterministic system design. FlexRay combines a time-triggered along with an event-triggered system. Based on an extended TDMA (time-division multiple access) media access strategy, a so-called communication cycle is set up, consisting of a mandatory static segment, an optional dynamic segment and two protocol segments called symbol window (optional) and network idle time (mandatory). Application cycles can be formed by one or more communication cycles, which are executed from start-up of the 42
network until its shutdown. A time-triggered communication system depends upon a common time base, the so-called global time. This time base has to be shared by all communication controllers in the network. In order to synchronize the clock, certain messages are marked as synchronization messages in the static segment. By means of a special algorithm, the local clock-time of a component, for example, a communication controller, is corrected in such a way that all local clocks run synchronously to a global clock. A bus guardian ensures that the data traffic moves in a timely fashion. Access to the communication bus has to be carefully managed, especially with regard to safety relevant applications. An independent component that protects a channel from interference is needed. It must limit the times that any communication controller can transmit to those pre-assigned intervals, in which it is allowed to do so. This component is the Bus Guardian. Basically, FlexRay systems allow to use bus guardians with independent clocks and are
Drive by Wire Systems
configured in a way that an error in the controller cannot influence the guardian and vice versa.
Composability As mentioned above, currently used systems are event-triggered, which means that whenever a station has data to send it tries to access the bus. Adding a new message is convenient to realize by assigning an identifier to it. The pitfall comes with the testing of such a system since any new message or change of the application modifies the behavior of the communication system. FlexRay puts a high demand on composability of the new communication system. Both the static and dynamic segments strictly adhere to the exclusive ownership of slots, a key foundation for the composability. As each ECU owns slots exclusively, it is possible to develop ECUs autonomously and to later integrate them without side effects. Figure 23 (a) and (b) illustrate the composability feature of FlexRay. The final FlexRay system architecture may take the form shown in Figure 24. A number of vendors already started marketing FlexRay systems. Some available hardware are: • • • • • •
National Instrument and Vector FlexRay CARDBUS card DeComsys FlexRay Node dSPACE MicroAutoBox Fujitsu Evaluation Kit TI DPS with FlexRay communication interface Freescale integrated and stand-alone FlexRay controllers
FlexRay Communication: Features and Challenges that Lie Ahead It offers a fault tolerant communication system for advanced automotive control applications. Fault-tolerant clock synchronization is achieved via a global time base. It offers Collision-free bus
access and guarantees message latency. It offers Message oriented addressing via identifiers. Scalable system fault-tolerance is achieved via the support of either single or dual channels. It has been chosen as the standard by most OEM’s. However, the following challenges still lie ahead with FlexRay development: • •
•
• •
• • • •
New protocol with new hardware & immature software tools Little experience both internally and externally with the development of a FlexRay communication system Nevertheless, internal development with TTP system has provided a good basic understanding of a real-time safety-critical communication system Tedious definition of information transfer using the FlexRay protocol Entering into a real-time world where the design and definition of our system is very different from past system designs Defining tasks to be executed within our system Defining worst-case execution times for these tasks Defining offsets for the execution of tasks and deadlines when tasks should be complete Must work closely with other suppliers to integrate into the overall FlexRay communication system of the vehicle
FlexRay System Design: Two Level Design Approach 1.
Cluster level design: At this design level, the physical cluster layout, the subsystems and the interaction between the subsystems are defined. Start with system requirement. Define the subsystems which are mapped to the nodes. Specify the signals provided by the subsystem, which are mapped to the position of the FlexRay frame. Generate the cluster specification.
43
Drive by Wire Systems
2.
Component level design: At this level, the precise subsystem interaction as specified in the cluster design is defined. Start with subsystem requirement specified during Cluster Level Design. Define the tasks that implement the behavior of the subsystem. The task specification must be consistent with the signal specification from the cluster design. Calculate the worst-case execution time.
Some Design Considerations •
•
Numbers of subsystems (nodes): It needs at least 4 nodes to make communication system true fault tolerant. Increasing the number of nodes will increase the cost. Nodes should be added only when it is necessary. Numbers of tasks within the subsystem: Number more available tasks gives the designer more scheduling flexibility. More tasks result in the schedule complexity and increase switching overhead. Trade off has
•
•
to be made between number of tasks and complexity/switching overhead. Communication cycle: The communication cycle needs to meet the real time control sampling time requirement. The cycle is also restricted by bus speed, the number of signals to be exchanged within the cycle, and number of tasks to be executed within the cycle. Considerable experiments need to be conducted to validate final communication cycle. Slot size: The larger the slot size, the fewer low level buffer reads and writes required. With larger slot sizes, the receiver might get many signals that it does not need, wasting buffer space. The proper slot size is application dependent, and is the judgment of the system engineer.
The culmination of a robust fault tolerant DBW system can be a drive by wire system that is designed based on FlexRay communication bus. One such design for steer by wire system is illustrated in Figure 25.
Figure 25. FlexRay based distributed steer by wire control system
44
Drive by Wire Systems
CONCLUDING REMARKS
REFERENCES
In what presented above, it is clear that drive by wire systems have clear benefits over their conventional counterpart in the area of safety and performance. DBW systems can, in fact, enhance the safety and performance of a vehicle equipped without any additional hardware, since these enhancements can be achieved through software upgrades. While drive by wire systems have made significant progress in fault tolerance and reliability, these systems, particularly the safety critical BBW and SBW systems, are still some steps away from full commercialization. One of the main reasons for this is the high cost of these systems due to multiple redundancies in sensors, actuators, microcontroller, and communications buses. As demonstrated in this chapter, analytical redundancy / model based fault tolerant control can reduce the total number of redundant components to certain extent. However, further reduction in the overall cost of the DBW system may still be warranted so that the automakers and their suppliers can competitively price these systems when compared with the conventional systems. The additional cost reduction can be achieved via innovative actuator design, manufacturing process optimization for microprocessors and communications buses, and inexpensive sensor development with diversity in the sensors. Historically the cost of microprocessors and communication electronics have seen significant drop in cost year over year. Therefore, the introduction of BBW and SBW systems in passenger vehicles is almost certain. However, the timing of such commercialization will largely depend on further research and development of these systems in order to address the aforementioned challenges and the willingness of the automakers and their suppliers to take on such a task to mass produce DBW systems.
Abe, M., & Mokhiamar, O. (2007). An integration of vehicle motion controls for full drive-by-wire vehicle. Proc. IMechE Part K: J. Multi-body Dynamics, 221, 117-127. Anon (2003, December). Cutting down on the chips. Automotive Engineer, (pp. 48-51). Anwar, S. (2005). Generalized Predictive Control of Yaw Dynamics of a Hybrid Brake-By-Wire Equipped Vehicle. International Journal of Mechatronics, 15(November), 1089-1108. Anwar, S. (2006). Anti-Lock Braking Control of a Hybrid Brake-By-Wire System. Journal of Automobile Engineering – IMechE Proceedings Part D., 220(8), 1101-1117. Anwar, S., & Chen, L. (2007). An Analytical Redundancy Based Fault Detection and Isolation Algorithm for a Road-Wheel Control Subsystem in a Steer-By-Wire System. IEEE Transactions on Vehicular Technology, 56(5), 2859-2869. Bajpai, G., Chang, B.C., & Lau, A. (2001, September). Reconfiguration of Flight Control Systems for Actuator Failures. IEEE AESS Systems Magazine, (pp. 29-33). Chang, S. C. (2007). Adoption of state feedback to control dynamics of a vehicle with a steer-bywire system. Proc. IMechE Part D: J. Automobile Engineering, 221, 1-12. Daniels, J. (2005). Practicalities. Automotive Engineer, 30(10), 33. Davis, B. (2001). Making the right connections. Professional Engineering, February 28, (pp. 36-39). Fowler, D. (2003). Down to the wire. The Engineer, (pp. 29-30). Gadda, C. D., Laws, S. M., & Gerdes, J. C. (2007). Generating Diagnostic Residuals for Steer-by-
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Wire Vehicles. IEEE Trans. on Control Sys. Tech., 15(3), 529-540. Haggag, S. Alstrom, D., Cetinkunt, S., & Egelja, A. (2005). Modeling, Control, and Validation of an Electro-Hydraulic Steer-by-Wire System for Articulated Vehicle Applications. ASME/IEEE Trans. on Mechatronics, 10(6), 688-692. Hasan, M. S., & Anwar, S. (2008). Sliding Mode Observer Based Predictive Fault Diagnosis of a Steer-By-Wire System. Proceedings of the 17th Int’l Federation of Automatic Control (IFAC) World Congress, Seoul, S. Korea. Hebden, R. G., Edwards, C., & Spurgeon, S. K. (2004). Automotive steering control in a split-mu maneuver using an observer based sliding mode controller. Vehicle System Dynamics, 41(3), 181202. Higgins, A., & Koucky, S. (2002). Mercedes pumps ‘Fly By Wire” brakes into new roadster. Machine Design, (p. 26). Hoseinnezhad, R. (2006). Position Sensing in Brake-By-Wire Calipers Using Resolvers. IEEE Transactions on Vehicular Technology, 55(3), 924-932. Isermann, R., Schwarz, R., & Stolzl, S. (2002, October). Fault tolerant drive-by-wire systems. IEEE Control Systems Magazine, (pp. 64-81). Kendall, J. (2005). Current Technology. Commercial Motor, 201(5117), 62-64. Lee, A. (2003). Driven by wire. The Engineer, 5(8), 28-29. Limpibunterng, T., & Fujioka, T. (2002). A new design approach for steer-by-wire system by dual port system. Vehicle System Dynamics, Suppl. 37, 197-208. Limpibunterng, T., & Fujioka, T. (2004). Bilateral driver model for steer-by-wire controller design. Vehicle System Dynamics, Suppl. 41, 381-390.
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Lu, X-Y., & Hedrick, J. K. (2005). Heavy-duty vehicle modelling and longitudinal control. Vehicle System Dynamics, 43(9), 653–669. Margolin, B. (1997). Analog, mixed-signal ICs steering drive-by-wire toward reality. Computer Design, 36(12), 31-34. Oh, S-W., Chae, H-C., Yun, S-C., & Han, C-S. (2004). The design of a controller for the steer-bywire system. JSME International Journal, Series C, 47(3), 896-907. Ross, P. (2003, February). Top Ten Techno-Cool Cars. IEEE Spectrum, (pp. 30-35). Rossi, C., Tilli, A., & Tonielli, A. (2000). Robust Control of a Throttle Body for Drive by Wire Operation of Automotive Engines. IEEE Transactions on Control System Technology, 8(6), 993-1002. Sameshima, H., Ogasa, M., & Yamamoto, T. (2004). On-Board Characteristics of Lithium Ion Batteries for improving energy regenerative efficiency. Quarterly report of RTRI, 45(2), 45-52. Segawa, M., Kimura, S., Kada, T., & Nakano, S. (2004). A study of the relationship between vehicle behavior and the steering wheel torque on steer by wire vehicles. Vehicle System Dynamics, Suppl. 41, 202-211. Setlur, P. J., Wagner, R., Dawson, D. M., & Braganza, D. (2006). A Trajectory Tracking Steerby-Wire Control System for Ground Vehicles. IEEE Transactions on Vehicular Technology, 55(1), 76-85. Shaheen, S., Heffernan, D., & Leen, G. (2003). A comparison of emerging time-triggered protocols for automotive X-by-wire control networks. Proc. Instn Mech. Engrs Part D: J. Automobile Engineering, 217, 13-22. Stanton, N., & Marsden, P. (1997). Drive-by-wire systems: some reflections on the trend to automate the driver role. Proceeding of Inst. Mech. Engrs. Part D, 211, 267-276.
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Wagstaff, I. (1999, April). Braking new ground. Automotive Engineer, (pp. 61-62). Yih P., & Gerdes, J.C., Modification of Vehicle Handling Characteristics via Steer-by-Wire. IEEE Trans. on Control Sys. Tech., 13(6), 965-976. Zheng, B., & Anwar, S. (2008). Fault Tolerant Control of the Road Wheel Subsystem in a
Steer-By-Wire System. International Journal of Vehicular Technology, Article ID 859571, doi:10.1155/2008/859571, 1-8. Zheng, B., Huang, M., Daugherty, B., & Anwar, S. (2006). Integrated Steer-By-Wire control and diagnostic system. Proceedings of the American Control Conference, Minneapolis, Minnesota.
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Chapter III
Electromagnetic Compatibility Issues in Automotive Communications Todd H. Hubing Clemson University International Center for Automotive Research, USA
ABSTtract Engineers and engineering managers involved in the design of automotive electronic systems need to have a basic familiarity with electronic noise and the electromagnetic compatibility (EMC) issues that influence the design and the performance of automotive systems. When EMC issues are addressed early in a product’s design cycle, the resulting designs often meet all EMC requirements without significant cost or performance problems. EMC problems detected after a product has been built and tested, on the other hand, can be very difficult and costly to fix. This chapter reviews automotive EMC requirements and discusses the design of automotive electronics for EMC. The objective of the chapter is to provide non-EMC engineers and engineering managers with basic information that will help them recognize the importance of designing for electromagnetic compatibility, rather than addressing electronic noise problems as they arise.
Introduction There are more electronic systems in the average car than there are in the average house. Dozens of computers, connected to sensors and actuators through kilometers of wiring, control everything from the engine to the brakes. Electronic systems determine the vehicle’s performance, handling,
and fuel economy. They also navigate, entertain, communicate and help to keep the driver and passengers safe and comfortable. Electromagnetic interference is a significant concern to the automotive system designer. Packing all of these electronic systems in close proximity and getting them to function reliably in all possible situations is becoming increas-
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Electromagnetic Compatibility Issues in Automotive Communications
ingly difficult. Microcontroller circuits generally operate at radio frequencies. They emit radio frequency noise and are susceptible to radio frequency fields. In an automotive environment, electronic systems must be able to function in the presence of intentional radio transmitters that are mounted on the vehicle, located along the vehicle’s route, or carried into the vehicle by the driver or passengers. This chapter provides an overview of the electromagnetic compatibility requirements that automobiles must meet. Significant electronic noise sources and coupling paths are described, and aspects of the major automotive communication buses that affect electromagnetic compatibility are reviewed. The chapter concludes with a discussion of design features that affect the electromagnetic compatibility of automotive systems.
AutomotiC REQUuir In order to help ensure that a vehicle will not have problems with electromagnetic interference, extensive electromagnetic compatibility (EMC) testing of each model is performed as the vehicle and its components are being developed. There are a number of EMC test standards for automobiles. The most widely referenced standards for unintentional electromagnetic emissions from vehicles are known as CISPR 12 (IEC, 2008) and CISPR 25 (IEC, 2002). CISPR, which is an acronym for Comité International Spécial des Perturbations Radioélectriques, is an organization that develops and supports a variety of international standards governing EMC. CISPR 12 defines test procedures and emission limits for radiated emissions from vehicles. CISPR 25 defines conducted and radiated emissions tests for vehicular components. CISPR 12 and CISPR 25 only address electromagnetic emissions. There are no CISPR standards for evaluating the susceptibility of vehicles or components to electromagnetic disturbances. Electromagnetic susceptibility (or immunity)
standards are developed and maintained by another international standards organization, the International Electro-Technical Committee (IEC). Various IEC standards describe procedures for evaluating the susceptibility of a vehicle or component to various disturbances such as strong RF (radio frequency) fields, power bus transients, and electrostatic discharge.
CISPR 12 According to the introduction in the standard, “CISPR 12 has been developed to serve the road vehicle and related industries with test methods and limits that provide satisfactory protection for radio reception.” It is designed to protect broadcast receivers operating at frequencies between 30 MHz and 1 GHz from unintentional electromagnetic emissions from a vehicle located 10 meters or more away. The standard applies to automobiles, trucks and boats, but not to aircraft or trains. The test procedure calls for the vehicle to be parked in a flat area free of buildings, trees or other objects that might reflect electromagnetic fields, as illustrated in Figure 1. Electromagnetic emissions from the vehicle are measured with an antenna located 10 meters away. Measurements are made with the vehicle’s engine idling at 1500 RPM, and also with the engine off but the rest of the vehicle powered on. Electric propulsion vehicles are measured on a dynamometer. For hybrid vehicles, a separate test is performed for each form of propulsion. Making electromagnetic measurements in an open field presents a few technical challenges including weather and ambient noise from local transmitters such as radio and television stations. For this reason, the CISPR 12 standard allows these measurements to be made in an absorber lined shielded enclosure (ALSE) provided these measurements can be shown to correlate to open field measurements. Most large automotive manufacturers operate or lease an EMC test facility with an ALSE. 49
Electromagnetic Compatibility Issues in Automotive Communications
Figure 1. CISPR 12 Radiated emissions test configuration ANTENNA
10 METERS EMITEST RECEIVER
CISPR 25 The CISPR 25 standard is designed to evaluate the conducted and radiated emissions from individual components and systems within a vehicle. It covers frequencies from 150 kHz to 1 GHz and includes an in-vehicle test procedure as well as bench-top tests. For the in-vehicle test, the test receiver is connected to an antenna mounted on the vehicle. This antenna is an existing radio antenna when measurements are made at frequencies within the radio receiver bandwidth. Out-of-band testing is done with a monopole antenna mounted in the same location. Each electronic system being evaluated is powered on and operated while monitoring the voltage induced at the antenna terminals. If the voltage exceeds the limit specified in the standard, the system under evaluation is non-compliant.
Bench-top tests are done on a 0.9-meter tall table with a metal top located in a shielded room as illustrated in Figure 2. The bench top is bonded to the wall or floor of the shielded room and the equipment under test (EUT) is placed on the table. Both the EUT and its wiring harness are insulated from the bench top by a 5-cm thick sheet of dielectric material. The EUT is powered through an artificial network (AN) that provides a stable well-defined power bus impedance at measurement frequencies. Another box attached to the wiring harness emulates all the sensors, actuators and communication devices needed to operate the EUT. CISPR 25 describes four bench-top tests, two that measure conducted emissions from the EUT and two that measure radiated emissions. The EMC test plan for a particular product may call for all or a subset of these tests to be performed. In one of the conducted emissions tests, the voltage
Figure 2. CISPR 25 radiated emissions ALSE test configuration
ALSE
ANTENNA AN
EUT 1 METER EMITEST RECEIVER
50
Electromagnetic Compatibility Issues in Automotive Communications
on each wire in the wiring harness is measured relative to the ground plane. In the other conducted test, the common-mode (i.e. net) current flowing on the wiring harness is measured. The two radiated emissions tests do not actually measure radiated emissions directly. In the first test, called “Radiated Emissions – ALSE Method,” the EUT is set up on the bench top as described above and measurements are made with an antenna located 1 meter away in the shielded room. Measurements made between 150 kHz and 30 MHz are done with a short monopole antenna mounted in a narrow ground plane that extends out from the bench top. Between 30 MHz and 200 MHz, a biconical antenna is used. Above 200 MHz, a log-periodic dipole array antenna is employed. At a 1-meter test distance, none of these antennas is in the radiated far-field of the EUT and its harness; therefore this procedure does not measure the actual radiated field strength. However, the voltage measured at the antenna terminals is an indication of the field strength at the antenna location, which (in many cases) correlates to the likelihood that a particular EUT will be the source of a radiated emissions problem in the vehicle. The second radiated emission test defined in CISPR 25 employs a device known as a TEM cell. TEM is an acronym for “Transverse ElectroMagnetic” and a TEM cell is a large waveguide designed to support only TEM mode propagation at frequencies below a cut-off frequency determined by the dimensions of the cell. As indicated in Figure 3, the EUT is placed inside the TEM
cell and configured in basically the same way that it would be configured on the bench top. The test receiver measures the voltage induced at the TEM cell termination and this voltage is compared to the limit specified in the standard. CISPR 25 specifies two possible configurations for the wiring harness, one that emphasizes emissions from the harness and one that minimizes the contribution from the harness. Like the ALSE tests, TEM cell measurements do not directly measure radiated emissions. Instead they measure the electric and magnetic field coupling to the TEM cell. These measurements generally do not correlate with the results of true radiated emissions tests, but they can provide useful information about the nature of the field coupling from the EUT, e.g. (Tippet & Chang, 1976), (Crawford & Workman, 1980), (Sreenivasiah et. al, 1981), and (Deng et. al., 2007).
Susceptibility (Immunity) Tests As indicated earlier, there are a number of published test procedures for evaluating the susceptibility of electronic components to electromagnetic disturbances. The International Standards Organization (ISO) maintains a number of standards for evaluating the performance of vehicles or vehicle components when subjected to electromagnetic interference. For example, (ISO 11452-2, 2005) and (ISO 11452-3, 2001) describe ALSE and TEM cell RF immunity tests with test configurations similar to the CISPR 25 bench-top and TEM cell tests. A transmitter drives the antenna (Figure 4)
Figure 3. CISPR 25 radiated emissions TEM cell test configuration
EUT EMI TEST RECEIVER
51
Electromagnetic Compatibility Issues in Automotive Communications
Figure 4. ISO 11452-2 radiated immunity test set-up
ALSE
ANTENNA AN
EUT SIGNAL GENERATOR
AMPLIFIER
or TEM cell to produce a strong EM field in the vicinity of the EUT. The strength of the applied field is increased until the limit is reached or a failure is observed. The limit depends on how critical the EUT is and what type of failure occurs. Minor “annoyance” failures may be allowed at relatively low field strengths, but failures that might affect the safety of the vehicle occupants must not occur even when the applied field strength is relatively high. (ISO 11452-4, 2005) describes a bulk current injection (BCI) test, where common-mode current is injected on the wiring harness, as illustrated in Figure 5. This type of immunity testing is very
popular in the automotive industry, because most automotive components are connected to a wiring harness and most EM susceptibility problems result from noise brought into the component through the harness. The waveforms used for the component-level susceptibility tests described above are continuous wave (CW) signals that are swept in frequency with different forms of modulation. (ISO 7637-2, 2004) and (ISO 7637-3, 2007) describe transient immunity tests, where the noise coupled to the harness is short pulses. (ISO 10605, 2008) describes an electrostatic discharge (ESD) test, where a simulator is used to create very short transients that
Figure 5. ISO 11452-4 bulk current injection test set-up
SIGNAL GENERATOR
EUT AMPLIFIER
AN
52
Electromagnetic Compatibility Issues in Automotive Communications
Table 1. ISO EM susceptibility test standards Document Number
Title
ISO 11451-2
Road vehicles — Vehicle test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 2: Off-vehicle radiation sources
ISO 11451-3
Road vehicles — Electrical disturbances by narrowband radiated electromagnetic energy — Vehicle test methods — Part 3: On-board transmitter simulation
ISO 11451-4
Road vehicles — Vehicle test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 4: Bulk current injection (BCI)
ISO 11452-2
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 2: Absorber-lined shielded enclosure
ISO 11452-3
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 3: Transverse electromagnetic mode (TEM) cell
ISO 11452-4
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 4: Bulk current injection (BCI)
ISO 11452-5
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 5: Stripline
ISO 11452-7
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 7: Direct radio frequency (RF) power injection
ISO 11452-8
Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 8: Immunity to magnetic fields
ISO 7637-2
Road vehicles — Electrical disturbances from conduction and coupling — Part 2: Electrical transient conduction along supply lines only
ISO 7637-3
Road vehicles — Electrical disturbance by conduction and coupling — Part 3: Vehicles with nominal 12 V or 24 V supply voltage — Electrical transient transmission by capacitive and inductive coupling via lines other than supply lines
ISO 10605
Road vehicles — Test methods for electrical disturbances from electrostatic discharge
are conductively coupled to all exposed metallic contact points on the EUT. Table 1 lists various automotive electromagnetic susceptibility test standards published by ISO. The Society of Automotive Engineers (SAE) also develops and maintains automotive EMC standards. The American National Standards Institute (ANSI), which represents the United States on international standards committees, has delegated their automotive standards writing activity to SAE. Many components of the CISPR and ISO automotive EMC standards were initially published as SAE standards. For this reason, there is a great deal of overlap between the SAE standards and other international standards. However, there are distinct differences that prevent similar standards from being used interchangeably. SAE Vehicular EMC Standards are listed in Table 2.
OEM-Specific Requirements The CISPR, ISO and SAE EMC standards are based on test practices developed by automotive companies to meet specific internal requirements. They are the result of many compromises and they have evolved slowly over the years. Most automotive companies adapt or supplement these procedures to meet their specific needs. For example, it is not uncommon for an automotive manufacturer to specify different test environments, test limits or excitation waveforms from those specified in the standards. Because of this, test laboratories that make EMC measurements for one automotive company may not be able to make the same type of measurements for another company. Many factors affect the electromagnetic coupling to and from an electronic system. Small variations in cable position, load impedance or
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Electromagnetic Compatibility Issues in Automotive Communications
Table 2. SAE EM susceptibility test standards Document Number
Title
SAE J551-11
Vehicle Electromagnetic Immunity—Off-Vehicle Source
SAE J551-12
Vehicle Electromagnetic Immunity—On-Board Transmitter Simulation
SAE J551-13
Vehicle Electromagnetic Immunity—Bulk Current Injection
SAE J551-15
Vehicle Electromagnetic Immunity—Electrostatic Discharge (ESD)
SAE J551-16
Vehicle Electromagnetic Immunity—Reverberation Chamber Immunity
SAE J551-17
Vehicle Electromagnetic Immunity—Power Line Disturbances
SAE J1113/1
Electromagnetic Compatibility Measurement Procedures and Limits for Components of Vehicles, Boats (Up to 15 M), and Machines (Except Aircraft) (50 Hz to 18 GHz)
SAE J1113/2
Electromagnetic Compatibility Measurement Procedures and Limits for Vehicle Components (Except Aircraft)— Conducted Immunity, 15 Hz to 250 kHz—All Leads
SAE J1113/3
Conducted Immunity, 250 kHz to 400 MHz, Direct Injection of Radio Frequency (RF) Power
SAE J1113/4
Immunity to Radiated Electromagnetic Fields-Bulk Current Injection (BCI) Method
SAE J1113/11
Immunity to Conducted Transients on Power Leads
SAE J1113/12
Electrical Interference by Conduction and Coupling—Capacitive and Inductive Coupling via Lines Other than Supply Lines
SAE J1113/13
Electromagnetic Compatibility Measurement Procedure for Vehicle Components—Part 13: Immunity to Electrostatic Discharge
SAE J1113/21
Electromagnetic Compatibility Measurement Procedure for Vehicle Components—Part 21: Immunity to Electromagnetic Fields, 30 MHz to 18 GHz, Absorber-Lined Chamber
SAE J1113/24
Immunity to Radiated Electromagnetic Fields; 10 kHz to 200 MHz—Crawford TEM Cell and 10 kHz to 5 GHz—Wideband TEM Cell
SAE J1113/26
Electromagnetic Compatibility Measurement Procedure for Vehicle Components—Immunity to AC Power Line Electric Fields
SAE J1113/27
Electromagnetic Compatibility Measurements Procedure for Vehicle Components—Part 27: Immunity to Radiated Electromagnetic Fields—Mode Stir Reverberation Method
SAE J1113/28
Electromagnetic Compatibility Measurements Procedure for Vehicle Components—Part 28—Immunity to Radiated Electromagnetic Fields—Reverberation Method (Mode Tuning)
SAE J1113/42
Electromagnetic Compatibility—Component Test Procedure—Part 42—Conducted Transient Emissions
test environment for example can result in large changes in the coupled energy at any given frequency. For this reason, it is not uncommon for a system tested in one lab to perform much differently than the same system tested in another lab. Also, since the laboratory test environment is different from the environment in which the system will ultimately be installed, a system which does well in a component-level test may still cause a failure in the vehicle-level test. Nevertheless, component level testing helps to identify EMC problems early in the design stage when it is still possible to make changes that do not significantly impact the product cost or development schedule. 54
Sourcof NOISE No other consumer product packs as many independently designed sources of electronic noise in such close proximity as the automobile. The intentional transmitters in a vehicle (e.g. cell phones, citizen’s band radios and wireless communications equipment) are responsible for their share of electromagnetic interference; however the electrical noise generated by the engine and the hundreds of communications and control circuits in a vehicle can be an even greater source of electromagnetic compatibility problems.
Electromagnetic Compatibility Issues in Automotive Communications
Microprocessors There are dozens of microcontrollers, digital signal processors and other kinds of microprocessors in a typical passenger car. Each processor’s speed is determined by a digital clock that typically switches at a frequency between a few megahertz (106 times per second) and a few gigahertz (109 times per second). Energy at the clock frequency and harmonics of the clock frequency is coupled to virtually every electrical conductor near or connected to the processor. The power generated at any given frequency associated with a clock is generally on the order of milliwatts (10 -3 watts) and most of this power is delivered to its intended destination and ultimately dissipated as heat. However, a few nanowatts (10 -9 watts) of radiated power are typically all that is necessary to interfere with intentional radio receivers in or near a vehicle. Therefore great care must be taken to control or contain the energy generated by the clocks and signals associated with every microprocessor in the vehicle.
PWM Signals A common way to transfer continuously variable levels of power to automotive actuators (e.g. the brightness of an LED, the position of a valve, or the speed of a motor) is using pulse width modulated (PWM) signals. Instead of varying the amplitude of a DC signal, PWM signals transmit pulses that switch between 0 and a constant amplitude at a fixed frequency. The width of the pulses determines the amount of power transferred to the
actuator, as illustrated in Figure 6. The primary advantage of this technique is that relatively little power is dissipated in the switching device (which is always full-on or full-off) and therefore it is possible to build relatively efficient controllers. However, PWM signals are a major source of electromagnetic interference in automobiles. Controlling an actuator with a DC amplitude setting generates very little high-frequency energy. PWM signals, on the other hand, generate significant amounts of energy at the switching frequency and at harmonics of the switching frequency. The fast transition times required for efficient switching of PWM signals can result in significant amounts of energy being created at frequencies that are 100 (or more) times higher than the switching frequency. High-frequency energy is much more easily coupled from one circuit to another and much more likely to interfere with radio receivers and wireless communication.
Power Bs Noise Even when the digital or PWM signals are carefully routed and isolated from other circuits, the active devices that produce these signals must draw current from the power supply. Switching devices tend to draw power from the supply in short bursts. These bursts of current and the corresponding dips in supply voltage can be propagated in an automobile’s power distribution bus, causing the bus to become a major conduit for electromagnetic interference. CISPR 25 specifies limits on the amount of noise that any given component is allowed to put on the power
Figure 6. PWM representation of an analog signal V
V
t
t
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Electromagnetic Compatibility Issues in Automotive Communications
bus. Nevertheless, anyone who has ever used an oscilloscope to measure the voltage on the power bus wiring in a modern automobile knows that spikes, dips, bursts and periodic high-frequency noise are common occurrences.
Engine Noise The electronic ignition circuitry in internal combustion engines generates sharp pulses that can be detected everywhere within and around the vehicle and can be especially strong in the engine compartment. Similarly, vehicles with electric drives employ motor controllers that generate very fast, high-current PWM signals. This type of noise can be difficult to deal with because the total amount of energy and the frequency content of these signals depend on many factors including the engine speed and load.
External Noise Sources The list of external noise sources that a vehicle might encounter is virtually endless, because cars and trucks are used for many different purposes and are driven in a wide variety of environments. External sources known to have caused automotive EMC problems include: • • • • • • • • •
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Radio and TV towers Overhead power transmission lines Electrostatic discharge generated by the occupants Electrostatic discharge generated by the vehicle-road interface Amateur radio equipment installed in the vehicle CB or fleet communications radios installed in the vehicle Police radar Nearby lightning strikes Cell phones in or near the car
The EMC standards described in the first section of this chapter are designed to emulate as many types of external sources as possible. However, the electromagnetic environment is constantly changing and it is difficult to anticipate every situation. Also, automotive manufacturers must balance the cost of designing for every possible situation against the impact of specific types of EMC-related failures in their market.
EM COUPLING Path Before discussing the relative advantages and disadvantages of one automotive design over another, it is important to have a basic understanding of how electromagnetic energy is coupled to or from electronic systems. Electromagnetic coupling can be divided into 4 categories: • • • •
Conducted coupling Electric-field coupling Magnetic-field coupling Radiated field coupling
The type of coupling most likely to occur depends on the frequency of the source, the impedances of the source and victim, and the proximity of the source and victim. Once a circuit is understood well enough to know which types of EM coupling are likely to be a problem, it is much easier to make design decisions that will minimize the chances of having an electromagnetic interference problem.
Conducted Coupling Conducted coupling (also called common-impedance coupling) can only occur when there are at least two conductors connecting the source to the victim. Examples of conducted coupling in an automobile include:
Electromagnetic Compatibility Issues in Automotive Communications
• • •
Lights dimming or systems resetting when the starter is engaged Power bus noise due to a motor controller being heard as static in the radio Damage to an ECU occurring as a result of arc welding on the vehicle
Figure 7 shows an example of how conducted coupling occurs. In this case, two systems in the automobile share the same current return conductor (the body of the vehicle). When the starter is engaged, it draws 20 amps of current from the battery causing the battery voltage at the terminals to drop to 6 volts. Other systems powered by the same battery must be able to function with an input of only 6 volts for a second or two while the engine is cranking. Any problems resulting from this momentary drop in power supply voltage are EMC problems. In the starter motor example, the two conductors are the power and ground conductors. Conducted coupling occurs because both the source and the victim circuits share a conducted circuit
path with a finite impedance (in this case, the battery resistance). Conducted coupling can also occur between two systems that both use body surface metal as their “ground” conductor. Currents from one system flowing in the body surface metal induce voltages across the metal that can affect the operation of other circuits that use the body surface metal as a “zero-volt” reference. Conducted coupling tends to dominate at low frequencies and is the only type of electromagnetic coupling that can occur at DC. Conducted interference problems are usually solved by reducing the current in the “culprit” circuit or by separating (isolating) the current paths in the culprit and victim circuits.
Electric-Field Coupling Electric-field coupling (also called capacitive coupling) is most likely to occur between highimpedance circuits. An example of electric-field coupling in an automobile is shown in Figure 8. A few electric field lines from a high-voltage igni-
Figure 7. Conducted coupling example
OTHER SYSTEMS
OTHER SYSTEMS
BATTERY
STARTER MOTOR
B ATTE R Y 0 .3 O H M S 12 V
+ -
IG N ITIO N S W ITC H R > > 0 .3 O H M S
O TH E R S Y S TE M S
S TA R TE R M O TO R 0 .3 O H M S
57
Electromagnetic Compatibility Issues in Automotive Communications
Figure 8. Electric-field coupling example ANTENNA
RADIO IGNITION
CM
V (t)
V N O IS E
IG N ITIO N C IR C U ITR Y
E LE C TR IC -FIE LD C O U P LIN G
tion circuit terminate on the AM radio antenna. Schematically, this can be represented by a mutual capacitance as illustrated in the lower part of Figure 8. The current induced in the AM radio receiver is proportional to C times dV/dt, where C is the mutual capacitance and dV/dt is the time rate of change of the excitation voltage. Electric-field coupling problems are generally solved by reducing the mutual capacitance between the culprit and the victim. This can be accomplished by increasing the distance between the two circuits or by shielding. It is important to note that electric-field shielding is different from magnetic-field shielding or enclosure shielding. The goal of an electric field shield is to terminate or redirect electric field lines before they reach the victim circuit. Figure 9 shows an example of how a shield might be used to eliminate the AM radio interference illustrated in Figure 8. A metal shield (in this case a metal hood) is placed in a position where it can intercept the electric field
58
R A D IO R E C E IV E R
lines emanating from the culprit circuit. The shield must be grounded to the culprit circuit’s reference conductor in order to effectively terminate the field lines. We could have protected the AM radio from this culprit and others by surrounding the antenna with a metal shield and grounding it to the AM radio’s reference conductor. However, this would have affected the antenna’s ability to perform its intended function. Whether it is better to shield the culprit or victim depends on many factors including cost and convenience, but the shield should always be grounded to the reference conductor that most effectively diverts the field lines.
Magnetic-Field Coupling Magnetic-field coupling (also called inductive coupling) is most likely to occur when the culprit is a low-impedance (high current) circuit. Figure
Electromagnetic Compatibility Issues in Automotive Communications
Figure 9. Electric-field shielding example
ANTENNA
RADIO IGNITION
C
V (t)
Figure 10. Magnetic-field coupling example
V N O IS E
M O TO R CURRENT
LO O P A R E A O F C IR C U IT M A G N E TIC -FIE LD C O U P LIN G
CM
HOOD
IG N ITIO N C IR C U ITR Y
I(t)
HO O D
E LE C TR IC -FIE LD C O U P LIN G
V N O IS E
R A D IO R E C E IV E R
10 shows an example of magnetic-field coupling between a motor driver and an audio circuit on a printed circuit board. Lines of magnetic flux passing through the circuit loop induce a voltage in the loop that appears across the input of the audio amplifier. A schematic representation of this coupling is shown in the lower part of Figure 10. In this case, the voltage induced across the amplifier input is proportional to M dI/dt, where M is the mutual inductance between the culprit circuit and the victim circuit and dI/dt is the time rate of change of the current in the culprit circuit. The mutual inductance can be reduced by increasing the distance between the culprit and victim or by diverting the magnetic flux lines with a shield. Unlike electric field lines, magnetic flux lines can’t be terminated on a conductor (or any other material). Since they cannot be terminated, they must be diverted. One of the best ways to divert low-frequency magnetic flux lines is to use steel (or a similar material with high magnetic permeability). Lines of magnetic flux prefer to
59
Electromagnetic Compatibility Issues in Automotive Communications
Figure 11. Low-frequency magnetic-field shielding with magnetic material
that are induced in these conductors. The eddy currents produce their own magnetic flux that opposes the flux creating the eddy currents. The net effect is that higher frequency lines of magnetic flux will flow around plates made of materials that are good conductors (e.g. aluminum or copper). Consider, for example, a magnetic interference problem between a PWM dome light circuit switching at 200 kHz and a nearby circuit board as illustrated in Figure 12. At 200 kHz, eddy currents induced in the copper plane of the circuit board are sufficient to divert the magnetic flux lines and prevent an interference problem.
Radiated Field Coupling
Figure 12. High-frequency diversion of magnetic field lines due to eddy currents
travel through steel rather than air when given the opportunity. As indicated in Figure 11, shields made of steel can effectively divert magnetic flux lines from a victim circuit when properly oriented. Note that simply putting a steel plate above or below the circuit would be relatively ineffective, because the magnetic flux lines would simply pass through the plate to the other side. At higher frequencies (e.g. 100s of kHz to MHz), magnetic flux lines are reluctant to flow through large conductors due to “eddy currents”
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Many people confuse radiated field coupling with electric or magnetic field coupling. In fact, the automotive EMC standards divide all tests into two categories, conducted and radiated, without any attempt to describe the specific type of coupling being measured. However, in order to properly design and troubleshoot for EMC, it is essential to distinguish electric and magnetic field coupling from truly radiated coupling. Radiated field coupling can only occur when the victim circuit is not in the near field of the culprit circuit. This generally means the victim circuit must be approximately a wavelength away (or farther) from the culprit. At 100 MHz, a wavelength in air is 3 meters. This means that radiated field coupling between circuits in an automobile is not likely to occur at frequencies below 100 MHz. Low frequency coupling will be predominantly electric or magnetic field coupling and all circuits and shields should be designed to minimize the type of coupling most likely to occur based on the impedance and environment of the systems involved. Radiated field coupling requires an “antenna” of some kind on both the culprit and victim circuits. Relatively efficient antennas may take the form of wires or other metallic objects. They can be made of copper, aluminum, steel or virtually
Electromagnetic Compatibility Issues in Automotive Communications
any highly conductive material. They can be bare metal or covered in plastic, but they all have one thing in common; efficient antennas are not small relative to the wavelengths they radiate. In fact, an efficient antenna must drive one metallic object with a voltage relative to another metallic object and NEITHER object can be very small relative to a wavelength. This means that good antennas at frequencies below a few GHz will be large enough to easily spot. Radiated emissions from automotive components are usually the result of common-mode currents induced on the wiring harnesses. Commonmode currents can be unintentionally induced on a wiring harness in two ways; differential signals on a harness can be transformed into commonmode currents by imbalances (asymmetries) in the harness structure or the signal source; or, electric or magnetic fields generated by the source can produce a voltage between the harness and the component that drives common-mode current on the cable. At frequencies above 30 MHz, a few microamps of common-mode current can be sufficient to cause radiated emissions that exceed some radiated emissions limits. The best way to minimize the chances of producing excessive radiated emissions is to make sure all of the large pieces of metal in your design
are held to the same potential. This generally means all wires, shields, heatsinks and enclosures should be bonded together, and all power and signal wires should have a high-frequency, low-impedance connection to these metal parts. The more high-frequency circuitry that is located between two metal objects, the more well-bonded or filtered those objects have to be. It is also a good idea to limit the frequency content of your circuits. Higher frequencies have shorter wavelengths and are therefore efficiently radiated by more of metal objects in the vehicle. Shielding to prevent radiated emissions or susceptibility can be difficult. It is generally necessary to completely surround the culprit or victim circuits with an enclosure that has no large apertures or seams. All wire penetrations through the enclosure must be carefully filtered. This is a common practice in military vehicles, but not in automobiles. Partial shields or shields with significant apertures, seams or unfiltered wire penetrations can actually increase the radiated emissions from a circuit board by providing the large metal objects needed to create an efficient antenna. Table 3 lists a few design guidelines that can help to reduce the chances of having a radiated emissions problem. However, successfully design-
Table 3. EMC design guidelines Design Guideline #1: Minimize the loop areas associated with high-frequency power and signal currents.
Digital circuit designers like to think of signals in terms of voltage. Signal integrity and EMC engineers must think of signals in terms of current. Signal currents always flow in loops. Keep these loops as small as possible.
Design Guideline #2: Do not split, gap or cut the signal return path
The safest rule-of-thumb is to provide one solid plane for returning all signal currents. In situations where it is expected that a particular signal is susceptible or is capable of interfering with the other circuits use a dedicated trace or wire to return that current to its source. Do not try to implement a scheme that “worked” in someone else’s “similar” design.
Design Guideline #3: Don’t Locate high-speed circuitry between connectors on a printed circuit board
High-speed circuitry located between connectors on a printed circuit board can easily develop potential differences of a few millivolts or greater between the connectors. These voltages can drive currents onto attached cables causing a product to exceed radiated emissions requirements.
Design Guideline #4: Control signal transition times
A well-formed digital signal will have a significant amount of power in the lower harmonic frequencies, but not so much power in the upper harmonics. Power in the upper harmonic frequencies is best controlled by controlling the transition times in digital signals.
61
Electromagnetic Compatibility Issues in Automotive Communications
ing and integrating the myriad electronic systems in modern automobiles cannot be accomplished by simply following a few design guidelines. It is important to hold regular design reviews with engineers who specialize in automotive EMC.
EMC ISSUES Wwith AUTutomoti NETtwork The dozens of microprocessors in the typical automobile must be able to exchange electronic information with various sensors, actuators and other microprocessors. This information can be conveyed by an analog signal level, a PWM signal, or digitally using one of the many data communications network protocols developed for automotive applications. There are many factors to consider when choosing an appropriate network for a given communications task including the amount of data, the importance of the data, and the cost of hardware necessary to implement the network. Electromagnetic compatibility is also a significant factor in this decision. This section discusses design aspects of each of the major automotive networks that have an impact on electromagnetic compatibility.
Controller Area Network (CAN) The CAN bus, developed in the mid-1980s, can be found in virtually every automobile manufactured today. It is also used in non-automotive applications such as factory automation and aerospace systems. The need to send and receive digital signals reliably in an electrically noisy environment was a significant factor driving the design of the CAN bus. CAN bus signal drivers and receivers employ a balanced, differential interface. The signals are represented as voltage differences between two signal carrying wires rather than as voltages relative to a reference ground. Both signal carrying conductors have the same impedance relative to ground and the bus is terminated with a
62
matched resistance at both ends. As a result, CAN signals are relatively unaffected by noise currents flowing in the ground structure. They are also less likely to produce significant radiated emissions, since the currents flowing in each signal wire are equal in amplitude and opposite in sign. CAN communications employ Cyclic Redundancy Checks (CRCs) and all messages are acknowledged by the recipient. In the event that transient electrical noise does couple to the circuit and cause a data error, these features reduce the likelihood of a communication failure. Figure 13 shows a basic CAN bus layout. EMC problems due to conducted coupling are rare in properly implemented CAN bus systems because the bus is isolated from the ground structure. However individual nodes can be disabled by conducted interference, and improperly designed nodes can introduce high-frequency noise on the bus that is ultimately radiated by the bus or by other nodes on the bus. CAN bus implementations should be tested to ensure that each side of the bus wiring has the same high impedance to ground when all terminations and nodes are connected.
Local Interconnect Network (LIN) The LIN bus was developed in the late 1990s as a lower-cost, lower-weight alternative to CAN. It employs a single-ended (as opposed to differential) signaling scheme, so it only requires one signal wire, as illustrated in Figure 14. Signal voltages are referenced to the ground structure (usually the vehicle frame) and signal currents return to the driver on this structure. LIN signals can also be transmitted over existing wires, such as a vehicle’s power distribution wiring. Since LIN bus data rates are less than 20 Kbps, radiated emissions are not a significant problem. There is simply not enough power in LIN bus signals at frequencies high enough to radiate efficiently. However, since the LIN bus uses the vehicle ground as the return conductor for its signal current, it is more sensitive to noise
Electromagnetic Compatibility Issues in Automotive Communications
Figure 13. Basic CAN bus layout NODE 3
NODE 4 NODE 1
NODE 2
Figure 14. LIN bus layout NODE 2
NODE 1
currents flowing on the body of the vehicle. To partially compensate for this vulnerability, LIN buses typically employ a 0-12 volt signaling amplitude. Stronger signal amplitudes mean higher levels of noise are required to cause an interference problem. Also, the bus termination impedance is high (nominally 1 kΩ), which reduces the levels of noise current that flow in the signal conductor. LIN bus communication takes place one message at a time between a master and slave node. Although it is not required by the LIN specification, transceivers typically have built-in ESD and overvoltage protection. Many also have risetime control and ground-shift protection.
FlexRay FlexRay was developed in the late 1990s to meet the demand for greater bandwidth and guaranteed response times. Like CAN, it employs a differential signaling scheme that can be implemented on twisted wire pairs with matched terminations. Unlike CAN, it employs a 3-state bus where signal levels can assume 3 different voltage (non-ground) levels. In its most robust configuration, all communication is point-to-point with connections to the rest of the network made through bidirectional repeaters called “active stars”. Passive star and
63
Electromagnetic Compatibility Issues in Automotive Communications
linear passive bus configurations are also possible. The maximum data rate is 10 Mbps. The FlexRay standard includes a document called the “EMC Measurement Specification” (FlexRay Consortium, 2005). This document does not specify test limits, but it does define EMC test procedures, test configurations, excitation waveforms, and failure criteria for evaluating the electromagnetic susceptibility of FlexRay networks. The FlexRay standard also specifies ESD protection levels for network processors.
MOST Media Oriented Systems Transport (MOST) is a high-speed networking scheme designed to meet the needs of audio and video entertainment systems in automobiles. Data is conveyed over optical fiber at rates as high as 23 Mbps. Optical fiber is immune to electromagnetic fields, but MOST networks are still susceptible to electromagnetic interference. The most vulnerable part of a fiber optic system is often the optical signal receiver. The high-gain, wide-band amplifiers required to convert flashes of light back to electrical signals can be relatively sensitive to conducted or electric-field coupled noise. These amplifiers must be carefully laid out, shielded and filtered to prevent system noise from obscuring the signal. The rest of the circuitry must also be carefully designed to avoid the electromagnetic interference and signal integrity problems that are commonly associated with circuits operating at these frequencies.
Bluetooth and other Wireless Communications Bluetooth is a wireless network commonly deployed in automobiles. It is a relatively low-power, short range wireless network that operates in the 2.40 – 2.48 GHz Industrial-Scientific-Medical (ISM) band. This is the same unregulated frequency band occupied by WiFi networks, microwave ovens, and a variety of other ISM devices. To
64
minimize interference, Bluetooth networks divide the band into 79 1-MHz channels and Bluetooth communication hops between channels. In this way narrowband interference that blocks only a few channels does not have a significant overall impact on communications. Of course, broadband or even out-of-band noise can disrupt Bluetooth networks if the power levels are sufficient. Automobiles must also co-exist with other wireless transmitters and receivers such as cell phones, WiMax devices, satellite communications devices and dedicated short range communications (DSRC) equipment. Unfortunately, current international EMC test standards do not adequately address potential interference problems with these devices. The technology has advanced faster than the standards organizations can react. Some manufacturers have developed their own tests and others test the vehicle by simply installing representative equipment and driving around. Fortunately, the transmitted power levels from most of these devices are relatively low. Also, the electromagnetic immunity is enhanced by the fact that the newer wireless communication technologies tend to be either spread spectrum or very narrow-band. Spread spectrum communication networks (like Bluetooth) shift in both time and frequency making them relatively immune to narrow-band interference, while narrow-band communication is relatively insensitive to interference occurring at frequencies other than the operating frequency. Nevertheless, as the number of interference sources in an automobile rises, the signal-to-noise ratio (and the range) of spread spectrum devices degrades and the probability of interfering with a narrow band transmission increases. New test procedures and design practices will be required to insure that future automobiles are electromagnetically compatible with an increasingly wide variety of wireless devices.
Electromagnetic Compatibility Issues in Automotive Communications
EC DESIign CONSIidration When automotive components and systems are carefully laid out and properly grounded and shielded, the automobile and its components can usually meet all EMC requirements with very little cost invested in EMC-specific design features. EMC problems with poorly designed components and systems, on the other hand, can be very difficult to fix without significantly impacting costs or development schedules. Therefore, it is critically important for automotive manufacturers and Tier 1 electronics suppliers to design for EMC early in the product development cycle and to have component and system design reviews on a regular basis.
Wiring Harnesses One aspect of automotive system design that deserves special attention is the design and layout of the wiring harnesses. Coupling from one circuit to another in a wiring harness is often referred to as crosstalk. It is important to pay careful attention to the currents that will flow out and back on each of the wires in a wiring harness. High-speed signals (with data rates greater than 1 Mbps) should always be routed on twisted wire pairs, shielded
twisted wire pairs or coaxial lines. Twisted wire pairs should be used for differential signals, and coaxial lines (with the shield grounded at both ends) should be used for single-ended signals. Low-frequency analog signals can be routed on a single wire using the body as a current return path provided these signals are able to tolerate the noise currents typically present on body surface metal. Low-frequency analog signals that are sensitive to conducted noise should use a wire for the return current that is isolated from ground at one or both ends.
Grounding and Shielding of Components Automotive components often have two separate reference grounds; one labeled “chassis” ground that connects directly to the component enclosure and automobile body surface metal, and another labeled “digital” ground that serves as the current return path for all of the digital circuits on the circuit board. Since both of these ground structures are rather large metallic objects (i.e. good antenna parts), it is important that they are held to the same potential at high frequencies. Even a few microvolts of noise between these two grounds at high frequencies can create a
Figure 15. An example of a circuit board with DC-isolated circuit and chassis grounds
C h a ssis co n n e ctio n to ch a ssis g ro u n d
W irin g H a rn e ss C h a ssis co n n e ctio n to ch a ssis g ro u n d
C a p a cito rs co n n e ctin g ch a ssis g ro u n d to th e d ig ita l re tu rn p la n e
D ig ita l R e tu rn P la n e C h a ssis G ro u n d P la n e
65
Electromagnetic Compatibility Issues in Automotive Communications
significant radiated emissions problem. Bonding them together directly in as many places as possible is the best way to accomplish this, however, sometimes it is necessary to isolate the chassis and digital grounds at low frequencies in order to keep noise currents that flow on the vehicle body from flowing across the circuit board ground. In other words, we want to isolate these grounds to prevent conducted (low-frequency) interference, but we want to bond them together at high frequencies to prevent a radiated emissions problem. This can usually be accomplished by routing different grounds on different layers of a multi-layer board and connecting them with many low-inductance capacitors. Figure 15 illustrates the grounding scheme in a typical multi-layer circuit board in an automotive application where chassis ground and digital ground must be DC-isolated. When there is a chassis ground plane on a printed circuit board, it is very important to ensure that this plane has a low inductance connection to both the chassis and to the digital ground plane, especially in the vicinity of external cable connections. Even a small, high-frequency voltage between the chassis and a wiring harness can result in excessive levels of radiated emissions. As described earlier, any shields that are applied to the product should be appropriate for the field coupling they are intended to suppress. Lowfrequency electric-field shields should intercept electric flux lines and route the displacement current harmlessly back to its source. Low-frequency magnetic-field shields should divert lines of magnetic flux away from the circuit being protected. High-frequency shielding enclosures must be carefully designed to eliminate leakage through seams and apertures, and all wire penetrations through the shield should be filtered.
Antennas High-end cars on the market today typically have about a dozen intentional antennas in them, a number that continues to rise each year. These antennas
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are used for remote keyless entry; AM, FM and satellite radio reception; tire pressure monitoring; GPS Navigation; cell phone communication and wireless data communication. Fortunately, most of these systems employ antennas and receivers that are relatively insensitive to out-of-band interference. However, the performance of each of these systems can be severely degraded by in-band interference, and the high-gain amplifiers in the front end of these systems can be overwhelmed by particularly strong transient or out-of-band continuous wave signals. Shielding is not usually a practical, cost-effective solution for protecting antennas and receivers. Thus it is important to be familiar with the relative sensitivities of a particular communication system and to locate potential sources of interference far enough from the antenna to minimize the potential for an interference problem.
EMC Design Reviews Many EMC problems that are first observed in vehicle prototypes are due to relatively obvious design flaws that could have been detected and easily corrected in the early stages of the vehicle design. Correcting problems after the first prototypes are built, however, can be relatively difficult and expensive. For this reason, it is very important to regularly review the design of the automobile and all electronic components on a regular basis throughout the product development process. Experienced EMC engineers can identify potential problems and recommend fixes that often add nothing to the product cost. No-cost EMC fixes include measures such as rerouting traces on a printed circuit board, changes in grounding, changes in cable routing, or changes in connector pin assignments. An EMC engineer may also recommend adding fixtures for filters or shields that are initially unpopulated and only used in the event that a problem arises later in the development cycle. This type of preparation costs nothing initially and can save substantial
Electromagnetic Compatibility Issues in Automotive Communications
amounts of time and money if EMC problems arise during prototype testing.
ConcluSIion For the first 60 to 70 years of its existence, the automobile was primarily a mechanical machine with few electric components. Automobiles today have dozens of microprocessors and kilometers of electric wire occupying a volume of a few cubic meters. As the number of electronic systems in automobiles rises, the potential for electromagnetic interference increases. Designing for electromagnetic compatibility is not difficult or expensive, but it is important to devote proper attention to EMC at every stage of an automobile’s development. Problems discovered late in the design cycle can seriously impact development schedules and product cost. This chapter has provided a basic overview of EMC issues affecting automotive communications for non-EMC engineers and engineering managers who work with automotive networks. Additional information on this topic can be found in (Paul, 2006), (Kaiser, 2005) or the Clemson Vehicular Electronics Laboratory website (http://www.cvel. clemson.edu).
REFERENCES Crawford, M., & Workman, J. (1980). Predicting free-space radiated emissions from electronic equipment using TEM cell and open-field site measurements. Proc. IEEE Int. Symp. on Electromagn. Compat., (pp. 80-85). Baltimore, MD. Deng, S., Hubing, T., & Beetner, D. (2007). Characterizing the electric field coupling from IC heatsink structures to external cables using TEM cell measurements. IEEE Trans. on Electromagn. Compat., 49(4) 785-791.
FlexRay Consortium. (2005). FlexRay Communications System, Physical Layer, EMC Measurement Specification Version 2.1. International Electrotechnical Commission – IEC. (2002). CISPR 25—Radio disturbance characteristics for the protection of receivers used on board vehicles, boats, and on devices—Limits and methods of measurement. IEC. (2008). CISPR 12—Vehicles, boats and internal combustion engines—Radio disturbance characteristics—Limits and methods of measurement for the protection of off-board receivers. International Organization for Standardization— ISO 11452-2. (2005). Road vehicles — Vehicle test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 2: Off-vehicle radiation sources. ISO 10605. (2008). Road vehicles — Test methods for electrical disturbances from electrostatic discharge. ISO 11452-3. (2001). Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 3: Transverse electromagnetic mode (TEM) cell. ISO 11452-4. (200). Road vehicles — Component test methods for electrical disturbances from narrowband radiated electromagnetic energy — Part 4: Bulk current injection (BCI). ISO 7637-2. (2004). Road vehicles — Electrical disturbances from conduction and coupling — Part 2: Electrical transient conduction along supply lines only. ISO 7637-3. (2007). Road vehicles — Electrical disturbance by conduction and coupling — Part 3: Vehicles with nominal 12 V or 24 V supply voltage — Electrical transient transmission by capacitive and inductive coupling via lines other than supply lines.
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Electromagnetic Compatibility Issues in Automotive Communications
Kaiser, Kenneth L. (2005). Electromagnetic Compatibility Handbook. CRC Press.
cell. IEEE Trans. on Electromagn. Compat., (23), 113-121.
Paul, C. R. (2006). Introduction to Electromagnetic Compatibility, 2nd. ed. Wiley-Interscience.
Tippet, J. C. & Chang, D. C. (1976). Radiation characteristics of electrically small devices in a TEM transmission cell, IEEE Trans. on Electromagn. Compat., 18(4), 134-140.
Sreenivasiah, I., Chang, D. C., & Ma, M. T. (1981). Emission characteristics of electrically small radiating sources from tests inside a TEM
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Chapter IV
Automotive Network Architecture for ECUs Communications Fabienne Nouvel Laboratory IETR-UMR, INSA, France Wilfried Gouret Laboratory IETR-UMR, INSA, France Patrice Mazério Laboratory IETR-UMR, INSA, France Ghais El Zein Laboratory IETR-UMR, INSA, France
ABSTtract This chapter deals with automotive networks and the emerging requirements involved by the X-by-wire and X-tainment applications. The introduction of ECUs (Electronic Control Units) has been driven by new market (like navigation, multimedia, and safety). Furthermore, the automotive industry has to face a great challenge in its transition from mechanical engineering towards mechatronical products. Combining the concepts of networks and mechatronic modules makes it possible to reduce both the cabling and the number of connectors. To connect the ECUs, a variety of network technologies are already widespread. A review of the most widely-used automotive networks and emerging ones is given first. To fulfill the increasing demand of intra-car communications, a new technique based on power line communication (PLC) is proposed and reviewed in the second section. On the other hand, there are several infotainment applications (like mobile phones, laptop computers) pushing for the adoption of intra-car wireless communications. Some of the most common wireless technologies that have potential to be used in the automotive domain are considered and different experimentations are presented. Finally, the challenges of these wired or wireless alternative solutions to automotive networks are highlighted.
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Automotive Network Architecture for ECUs Communications
Introduction Fieldbuses are a family of automotive communication networks that have evolved as a response to the demand to reduce cabling costs in factory automation systems. Moving from a situation in which every controller has its own cables connecting the sensors to the controller to ECU sharing a bus, costs could be cut and flexibility could be increased. Pushing for this evolution of technology was the fact that the number of cables in the system increased as the number of sensors and actuators grew. Furthermore, the ECUs share their CPU power and knowledge with other controllers. Several fieldbus technologies, usually very specialised, were developed by different manufacturers to meet the demands of their application. The standardisation process stabilized not until the mid 90’s with CAN standardized in1993 by the International Standardisation Organisation (ISO) (Johansson, Törngren, & Nielsen, 2005). Indeed, today’s vehicle networks are not just collections of discrete, point-to-point signal cables. They distribute the electronic systems according to their domain sharing networks. Replacing rigid mechanical components with dynamically configurable and even reconfigurable electronic elements triggers an almost organic, system wide level of integration. Furthermore, the amount of cables is drastically reduced by using these fieldbuses. As a result, the cost of advanced systems should plummet. Furthermore, highly reliable and fault-tolerant electronic control systems, Xby-wire systems do not depend on conventional mechanical or hydraulic mechanisms. They make vehicles lighter, cheaper, safer, and finally more fuel-efficient. In (Len & Hefferman, 2001), the authors demonstrate the advantages of X-by-wire and embedded networks. For example, Leen and Hefferman write “X-by-wire steering systems under
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development will replace the steering column shaft with angle sensors and feedback motors. This removal will improve driver safety in collisions and allow new styling freedom. It will also simplify production of left- and right-hand models. It is natural to add advanced functions to such electronic systems. For example, consider systems that reduce steering-wheel feedback to the driver. In mechanical steering systems, the driver actually feels the vehicle losing control in unstable conditions and can react appropriately. These self diagnosing and configurable systems adapt easily to different vehicle platforms and make the diagnosis easier” (Len & Hefferman, 2001, p.90). Figure 1 represents the future topology of embedded networks inside luxury cars. We can notice the different multiplexed networks which may need gateways to communicate between different application domains. Considering these large number of distributed ECUs, it makes the automotive system complex in many ways. Several different networks are used to address the different levels of communications requirements, including safety-critical, and faulttolerant. High bandwidth becomes necessary to interconnect these systems with predictability. This last issue is a great challenge for car manufacturers. To reduce this complexity, without reducing safety, it is desirable to commit to a limited set of networks. However, it is not in the near future that the number of networks can be reduced to only one or two as such technology would provide the properties supporting the most demanding automotive systems. It would probably make it too expensive. Hence, it is more likely that a few network technologies will be used while providing timeliness and fault tolerance. The next section gives an overview of networks used today in the automotives, identifying their strengths and possible drawbacks.
Automotive Network Architecture for ECUs Communications
Figure 1. Network vehicle architecture (source: Automotive Systems Research Group, 2008)
EVOolution of automoti nETtworking t The purpose of this section is to give a description of the most representative networks for different main domain of application. We will focus on the three most common vehicle networks, namely LIN, CAN and Flexray. These embedded networks have both increased the functionality and decreased the amount of wires. However, the usage of different wires for the different networks still has the disadvantage of heavy, complex and expensive.
The Local Interconnect Network: LIN Conceived in 1998, the LIN consortium comprises many car manufacturers like Audi, Volvo, BMW (“LIN Consortium”, 2003). LIN is an inexpensive slow and serial bus used for distributed body control electronic systems in vehicle. It enables effective communication for sensors and actua-
tors where bandwidth, speed and versality are not required (i.e inside mechatronic based subsystems generally made of an ECU and its set of sensors and actuators). LIN is commonly used as a sub bus for CAN and Flexray. A LIN network is based on one master node and a couple of LIN slaves (up to 16 over 40 meters line length). The communication in an active LIN network is always initiated by a master task: the master sends out a message header that comprises the synchronisation break and message identifier (frame header). Exactly one slave is activated upon reception and filtering of the identifier (up to 64), which starts the transmission of the response, up to eight data bytes. The frame response may be sent by one LIN slave or LIN master. Each LIN frame is composed of a frame header and a frame response. Figure 2 depicts the LIN frame format transmitted during its frame slot. The master node decides when and which frame shall be transmitted according to the schedule table containing the transmission order. At the
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Figure 2. the LIN frame (source: Navet, Song, Simonot-Lion & Wilvert, 2005).
moment a frame is scheduled for transmission, the master sends the header inviting a slave node to send its data in response. Any node interested can read a data frame transmitted on the bus. The reliability of LIN is high but it does not have to meet the same levels as CAN. The LIN can be implemented using just a single wire, while CAN needs two. The physical layer (PHY) supports a data rate equal to 20 kb/s (due to electromagnetic limitations) but other transmission supports enabling higher data rates are possible. Another representative of low-cost network is TTP/A (Kopetz & all, 2002). It implements master-slaves exchanges where the slave answers the master’s request with a data frame having a fixed length. However, to the best of our knowledge, TTP/A is not currently in use in production cars. On the other hand, LIN is widely used in middle range cars but it can not support high data rate as required by devices like portable DVD players or multimedia applications.
The Control Area Network: CAN The Basic CAN The Controller Area Network (CAN) (“CAN”,2001; “Road Vehicles”, 2004) is a widely communication fieldbus used in automotive and other real time applications. It is a serial communications protocol which efficiently supports distributed realtime control with a middle level of security. In automotive electronics, engine control units, sensors, anti-skid-systems, etc. are connected us-
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ing CAN with bit rates up to 1 Mbit/s. However, in today’s car, CAN is used as an SAE (Society of Automotive Engineers) class C (classification defined in (“J2056/2 Survey”, 1994)) network for real time control in the powertrain and chassis domains (at 250 or 500 kb/s). It is also implemented as an SAE class B network for the electronics in the body domain (at 125 kb/s). In CAN, data is transmitted in frames containing between 0 and 8 bytes of data and a number of control bits. A CAN frame is labelled by an identifier whose numerical value determines the frame priority. Depending on the CAN format (standard CAN 2.0A or extended CAN 2.0B) the size of the identifier is 11 bits (CAN 2.0A) or 29bits (CAN 2.0B). Between CAN frames sent on the bus, there is also a 3 bit inter-frame space. The standard CAN frame format (and the inter-frame space) is depicted in Figure 3. CAN is a collision-avoidance broadcast bus (CSMA/CA for carrier sense multiple access with collision avoidance), which uses deterministic collision resolution to control access to the bus. It implements a fixed-priority based arbitration mechanism that can provide real time guarantees and that is amenable to timing analysis. As distributed real time systems become more and more complex, the computing power is steadily growing, and the number of ECUs attached to CAN buses is growing. Thus CAN’s maximum speed of 1 Mbps can lead to performance bottlenecks. Hence, methods for increasing the achievable utilisation are needed, e.g., novel analysis methods that allow increased utilisation
Automotive Network Architecture for ECUs Communications
Figure 3. The CAN frame (source: Nolte, 2006)
while guaranteeing timing requirements to be fulfilled, and novel approaches to schedule CAN.
Derivative Products and CAN Optimization In (Nolte, 2003; Nolte, 2006), the author addresses two issues to increase real time. First, he investigates a novel way of scheduling the CAN through a server-based scheduling. This increases the flexibility of CAN, by providing efficient handling of sporadic and aperiodic message streams. He also studies the bit stuffing which could degrade the real time and data rate. The bit-stuffing mechanism of CAN causes the message frame length to vary, depending on the original bit-pattern of the CAN frame Currently, time-triggered communication systems are being considered for use in X-byWire applications. In this category, multi-access protocols based on time division multiple access (TDMA) are particularly well suited. Time-Triggered communication on CAN is specified by TT-CAN (“Road Vehicles”, 2004), a standardised session layer extension to original CAN. In TTCAN, the exchange of messages is controlled by the temporal progression of time, and all nodes are following a pre-defined static schedule. One node, the master node, is periodically (or on the occurrence of a specific event) transmitting a specific reference message, which acts as a reference in time. All nodes in the system are synchronising with this message, which gives a reference point in the temporal domain for the static schedule of
the message transactions. The static schedule is based on TDMA where message exchanges may only occur during specific time slots or in time windows. Hence, the master’s view of time is referred to as the network’s global time. TT-CAN appends a set of new features to the original CAN, and being standardised, several semiconductor vendors have started manufacturing TTCAN compliant devices. Probably, carmakers might consider the use of TTCAN for some systems during a transition period as FlexRay technology seems to be in a very strong position for becoming the standard in the industry.
The Flexray Protocol FlexRay (“Flexray Consortium”, 2004) is a communication system developed by a consortium founded in 2000, including both car and semiconductors manufacturers. The consortium members realized that despite the numerous automotive communications protocols out on the market, none would fulfil future automotive control requirements. FlexRay is a fault-tolerant protocol designed for high-data-rate, advanced-control applications, such as X-by-wire systems. The protocol specification promises time-triggered communications, a synchronized global time base, and real time data transmission with bounded message latency. An overview of the FlexRay frame format is given in Figure 4. The frame consists of three segments. These are the header segment, the payload segment containing up to 254 bytes of
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Figure 4. Flexray frame (source Flexray Consortium, 2004)
data, and the trailer or CRC segment. A node shall transmit the frame on the network such that the header segment appears first, followed by the payload segment, and then followed by the trailer segment, which is transmitted last. The header of 5 fields includes the identifier, the cycle count and the length of the data payload. Communication is done in a communication cycle consisting of a static part and a dynamic part, where each of the parts may be empty. The sending slots are represented through the identifier (ID) numbers that are the same on both channels. The sending slots are used deterministically (predefined TDMA strategy) in the static part. In the dynamic part there can be differences in the phase
on the two channels. Nodes that are connected to both channels send their frames in the static part simultaneously on both channels. It is possible to send different data in the same sending slot on different channels (Figure 5). An interesting feature of Flexray is that it can provide scalable dependability i.e., the “ability to operate in configurations that provide various degrees of fault tolerance.” Indeed, the protocol allows for mixing links with single and dual transmission supports on the same network, or with different fault-tolerance capability with regards to clock synchronization, etc. Proposed applications include chassis control, X-by-wire implementations, and body and powertrain systems. Currently,
Figure 5. Communication cycle ( from (“Flexray Consortium”, 2004))
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FlexRay can handle communications at 10 Mbps. In the automotive context, where critical and non-critical functions will increasingly coexist and interoperate, this flexibility can prove to be efficient in terms of cost and reuse of existing components. Flexray is considered by manufacturers as the backbone network for the other networks like CAN or LIN. It is supposed to be foremost used in safety-critical X-by-wire applications. However, this network still uses specific wires, which do not achieve compatibility with other networks. So, gateways are necessary to transfer information from ECUs connected on a domain network to ECUs connected on other networks. Those gateways could introduce latency, errors, bottlenecks, and so on. Other fault-tolerant networks have been developed, namely TTP and TT-CAN, but it seems they are not the best choice for automotive manufacturers due to limited flexibility, high costs and conflicting interests (Nolte, 2006).
Some Other Automotive Networks Two other major networks have been developed for emerging multimedia applications, namely MOST (“MOST”, 2006; Nolte, 2006), and IDB 1394 (Navet, Song, Simonot-Lion & Wilvert, 2005; “IDB Forum”, 2008). MOST, initiated in 1998, is expected to be available with speeds of 150 Mbps. It is using plastic optical fibers as communication medium (and coax cables) and supports up to 64 devices (nodes). It is sharply more expensive than other networks. The reader can have more details at (“MOST Cooperation”, 2008). On the other hand, IDB Customer Convenience Port 1394 (IDB-1394), an automotive version of IEEE 1394, supports data rate of 100 Mbps and supports up to 63 nodes over a specific six-wire cable. The IEEE 1394 interface is a serial bus interface standard, for high-speed communications and isochronous real time data transfer, frequently
used in digital audio and digital video. The interface is also known by the brand names of FireWire. In the automotives, IDB-1394 is a digital network that runs at speeds of up to 400Mbps carrying high quality video, multi-channel sound and high speed data for vehicle applications. Designed for entertainment and communication applications, IDB-1394 enables devices to operate digitally carrying multiple programs of up to high definition video along with multi-channel audio between devices on a vehicle. In (“IDB1394”, 2008), an IDB-1394 demonstrator has been presented and focuses on the advantages of such solution. These two systems answer to the actual automotive trends namely multi-media networks, data storages, multiple users, navigation and entertainment applications. However, these two high speed solutions need specific wires and allow only peer to peer communications. They answer specifically to multi-media applications requirements. It doesn’t seem to be a so much flexible seamless solution. Some common technologies are discussed in (Navet, 2008) and (Nolte, 2006).
Conclusion Table 1 sums the different embedded protocols used in today vehicles. Considering these specific domain embedded networks, we can observe that each solution uses its specific wires and communication system. We can see the wide diversity of the solutions and the necessity to find a limited set of networks which answers to these multiple applications. An attractive solution to reduce the wires is the power line communication (PLC) using the power lines (12/42V) to transmit both the power and the messages without functional barriers domain. It can answer the vehicles requirements namely cost, decrease of the amount of wires, flexibility and bandwidth. First of all, we will describe the power line channels and the available PLC techniques in the next section.
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Table 1. Embedded automotive networks Networks
LIN
CAN
Flexray
MOST
IDB-1394
Data rate
20 Kbps
1Mbps
10 Mbps
25 Mbps -> 150 Mbps
100 Mbps
Medium Access
Master/slave TDMA
CSMA/CA
Master/salve TDMA
Peer to peer
Peer to peer
Number of nodes
16 / 40 meters
32 / 40 meters
22/passive star
64 /fiber
63/fiber
Security/fault tolerant
low /no
medium/some
high/yes
some /no
some /no
Applications
Sub-networks
Body domain, power train
X-by-wires
Mutli-media
Mutli-media
PowER lin communication (PLC) ntwork: an altrnati olution Considering the current embedded vehicle networks, these fieldbuses require different wires of several kilometres. It leads to increasingly complex network architectures. Thus gateways are necessary to switch from one network to another one. This increases the propagation time and does not guarantee real time. One solution to reduce the amount of wires would be to use the PLC technology that is currently being developed for indoor networks to transmit information or at least some of it, over the 12V power distribution system found in automobiles (Rubin, 2002). This solution would reduce the amount of wires as well as simplify the network architecture. The possible applications of automotive PLC are very wide, extending from low-speed data buses for activating actuators to high-speed multimedia applications. To our knowledge, there are no cars actually that implement PLC network instead of dedicated protocol buses. Different studies have been carried out to demonstrate the PLC feasibility, like the multi-user and low data rate system in (Nouvel, El Zein & Citerne, 1994). It is possible to interface PLC with CAN bus, as described in (Beikirch & Voss, 2000). Other low data PLC solutions have been studied in
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(Schiffer, 2000) and (Huck, Schirmer & Dostert, 2005). Recently, different applications have been tested like in (“Valéo”, 2006) which is a video transmission between a camera at the back of the vehicle and a screen placed on the dashboard up to 30 frames/s. Although high data rate and flexibility obtained for indoor domestic PLC are proven, it is not possible to apply them directly to cars because the geometrical characteristics and wires topologies are totally different. Moreover, the PLC channels are affected by the variable activation schedules of electrical functions, such as brakes, indicators or ABS, which produce sharp modifications in the circuit’s load impedances over brief time intervals, as presented in (Lienard, Carrion, Degardin & Degauque, 2008). To optimize high bit-rate communication, the PLC channel transfer function must be carefully studied because it is frequency selective. The most promising candidate techniques for PLC transmission are spread spectrum combined with code division multiple access (CDMA) and orthogonal frequency division multiplexing (OFDM). The first one has been studied in (Nouvel, El Zein & Citerne, 1994), in which the PLC network offers about 50 kbits/s per user, combined with multiplexing. On the other hand, OFDM has been adopted for different wireless wide area or personal area networks such as
Automotive Network Architecture for ECUs Communications
in IEEE 802.11a/g and IEEE 802.16a (Hara & Prasad, 1997; Heiskala & Terry, 2002). The main advantages of this technique are well known and included robustness, high spectrum efficiency in frequency-selective, flexible spectrum adaptation and time-variant fading channels and capability of portable and mobile reception. Furthermore, OFDM allows high data rate and can be combined with CDMA. The choice of the modulation is driven by the PLC channel and the different parameters are optimised according to the channel behaviour: the bandwidth, the modulation (number of sub-carriers), the coding, the guard interval, and so on. Below the embedded wiring harnesses are presented in order to point out the variations on the PLC channels. Then, the OFDM HomePlug standard used in indoor PLC is presented and is studied for the embedded vehicle PLC.
Transfer Function and Propagation Channel Above all, it is very important to investigate both channel capacity and noise environments on the power line channel. As such a channel is not ideal, it is characterised by noise disturbance, loss, interference of impulsive type, and also attenuation. Otherwise, the goal of the measurements on the vehicle is to investigate the influence of the state of the elements connected on the power line. To determine the capacity of the channel through the cables for a PLC transmission, many transfer functions between two nodes have been measured in the study carried out by (Lienard, Carrion, Degardin & Degauque, 2008; Degardin, Laly, Liénard & Degauque, 2006). The authors describe the aims of the tests and the measurements set up. The most important results are given here. In order to analyse the battery effects, two types of channels must be considered, depending on the network architecture between a transmitting point TX and a receiving point RX. A path
is labelled “direct” if the shortest connection between the modems does not pass through the battery, a path is labelled “indirect” in all other cases. This distinction is based on the fact that in the frequency range, the battery acts as a very low impedance which partially short circuits the transmission line. The schematic representation of the network used in the simulation and measurements is given in Figure 6. Two paths, D1D3 and D2D3 are considered, corresponding to a direct and an indirect path, respectively. In this test bed, the distances D1D3 and D2D3 are about 8 meters and 10 meters respectively. Furthermore the number of wires inside cable bundles varies from 1 to 30. The scattering matrix was taken on the 12V power line while moving through urban and suburban traffic. Examples of transfer functions for direct and indirect connection in the frequency range 500 kHz-30 MHz are shown in Figure 7. The results
Figure 6. Testbed for power line measurement
Figure 7. Power line channel measurements
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show the insertion losses over the [0-30] MHz bandwidth are about -15 dB and -36 dB (influence of the battery) for direct and indirect path respectively. The transmitted signal will be more attenuated in indirect path than direct path. To carry out the statistical study, measurements were taken while the electronic equipments are alternatively switched ON and OFF. Figure 8 represents the variations of the transfer function between these two nodes in the vehicle according to their state (ON/OFF) and/or other elements present on the same cable. ‘Hi’ refers to different configurations of electric equipments (ON/OFF). One can see that the power line is very frequency selective and the attenuation depends not only on the distance between nodes but also on the states of each node. The selectivity of the channel is coupled with attenuation due to the distance between the nodes. Deep fading in many frequency bands, especially in the PLC band [2 - 30 MHz] used in residential area appears. Liénard, Carrion, Degardin & Degauque (2008) have then focus on the coherence bandwidth and delay spread. The statistical analysis shows that the coherence bandwidth is greater than 800 kHz for direct path and 400 kHz for
indirect path that have to be compared with the OFDM/HomePlug (“Home Plug V1.0”, 2008 ) subcarrier spacing of 195 kHz. The delay spread remains under 380 ns for all the geometrical and electrical configurations. This value is ten times smaller than the cyclic prefix duration recommended by the HomePlug standard. In (Degardin, Laly, Liénard & Degauque, 2006), the authors have also considered the impulsive noise. The analysis of the background noise measured on the vehicle DC line during cruising, shows that this noise can be modelled by a white Gaussian noise with a Power Spectral Density (PSD) of -130 dBm/Hz. Additional narrow and wide band noise generated by the electronic devices will also disturb the transmission. Figure 9 displays impulse noise measurements. A classification of the pulses into two classes has been proposed: the first type is a single transient or pulse, while the second one is a burst characterized by a succession of elementary pulses. To overcome this noisy PLC environment, the used of OFDM modulation technique can be investigated. Using OFDM has in fact many benefits as it can cope with deep fading, provides good interchannel interference performance and is
Figure 8. a. Testbed setup, b. Channel variation according to loads
a.
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b.
Automotive Network Architecture for ECUs Communications
Figure 9. Typical noise records in a vehicle
good at minimizing the effect of in-band narrow interference. Furthermore, one other requirement for PLC is the automotive EMC (electromagnetic compatibility) constraints Ray_CISPR25 (“CISPR25”, 2002) which allow transmission in the [0.15-30] MHz bandwidth under 50 dBµV (-35dBm/Hz).
HomePLug V 1.0 Sandard As OFDM (Hara & Prasad, 1997) is used in different communication systems we present in this section, a brief overview of this modulation widely used in both wired and wireless PHY layers is given. OFDM is a frequency-division multiplexing (FDM) scheme using the fast Fourier transform (FFT) to generate the signal. A large number of closely-spaced orthogonal sub-carriers are used to carry symbols. The incoming stream is divided into several parallel data streams or channels equal to the number of sub-carriers. Each sub-carrier is modulated with a conventional modulation scheme (such as QPSK (quadrature phase-shift keying)) at a low symbol rate, maintaining total data rates similar to conventional single-carrier
modulation schemes in the same bandwidth.The primary advantage of OFDM over single-carrier schemes is its ability to cope with severe channel conditions, for example attenuation of high frequencies in a long wire, narrowband interference and frequency-selective fading due to multipath, without complex equalization filters. Channel equalization is simplified because OFDM may be viewed as using many orthogonal slowlymodulated narrowband signals rather than one rapidly-modulated wideband signal. To eliminate intersymbol interference (ISI), guard interval is inserted between symbols making it possible to handle time-spreading. OFDM has been adopted for different wireless wide area or personal area networks (for example in IEEE 802.11a/g) or over copper wires (for example xDSL). It is used in new applications such as high definition video and audio broadcasting. In 2000, a coalition of manufacturers has established a new protocol HomePlug 1.0 that enables the establishment of an Ethernet-IP class network over power line channels (“Home Plug V1.0”, 2008). The HomePlug process is based on an OFDM technique whose major advantage for
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Table 2. Different transmission modes: coding and bit rate Transmission Mode
Modulation
1
DBPSK
2
DQPSK
3
DQPSK
Coding ½ code rate Reed Solomon interleaver (t=8) 23/29 to 238/254 and convolutional encoder (constraint length = 7)
Maximum bit rate 4.53 Mb/s 9.06 Mb/s 13.59 Mb/s
Figure 10. HomePlug V1.0 PHY frame format (source: Home Plug V1.0, 2008)
Figure 11. HomePlug spectrum in the [1-25] MHz bandwidth and transmitted signal
128 sub car r ier s synchr o 84 car r ier s for data
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the embedded PLC application is to cope with the frequency selectivity of the power line channel caused by the multiple reflections of the loads connected to the power grid and by the coupling to the other cables placed in the same bundle. The modulation is based on 128 sub-carriers equally spaced from 4.3 MHz to 25MHz, in conjunction coding applied before differential encoding. The differential encoding allows simplifying the Rx part by suppressing the channel equalization. Only 84 carriers are used in the transmission bandwidth, with DBPSK (differential binary PSK), DQPSK (differential QPSK) or ROBO modulation ( a DBPSK with heavy error correction) on each subcarrier. HomePlug uses CSMA/CA protocol to access to the network. Figure 10 represents the PHY frame format as Figure 11 represents the frequency response and the transmitted signal over a 220 V AC network. All the coding parameters are summarized in Table 2. Recently, the HomePlug AV has been introduced and will be the second major standard released by the HomePlug Powerline Alliance (Gavette, 2006; Afkhamie, Katar, Yonge, & Newman, 2005). While HomePlug V1.0 was designed mainly to distribute broadband internet access at home, the HomePlug AV’s objective is to distribute multi-media content within the house as well as data. The PHY layer still operates in the frequency range of [2 – 28] MHz and provides a 200 Mbps PHY channel rate (150 Mbps net information rate). Long OFDM symbols with 917 usable carriers (tones) are used in conjunction with a flexible guard interval. Modulation densities from BPSK (binary PSK, 1 bit of information per carrier per symbol) to 1024 QPSK (10 bits of information per carrier per symbol) are independently applied to each carrier based on the channel characteristics between the transmitter and the receiver. Experimental systems of HomePlugAV have been field tested in houses, suggesting that on average HomePlug AV system achieves 10 times the data rate of a 1.0 system.
Nowadays, the HomePlug Alliance and the IEEE (“IEEE 1901”, 2008) are strongly committed to delivering a single mature solution that will be endorsed by the IEEE 1901 work group as the baseline standard. This alliance will propose a standard for high speed (over 100 Mbps at the physical layer) communication devices via alternating current electric power lines, so called Broadband over Power Line (BPL) devices. It will use transmission frequencies below 100 MHz. This solution could be investigated in an automotive environment. Some papers have compared HomePlug V1.0 and HomePlug AV with the indoor wireless system (Lee, Latchman, Newman, Katar & Yong, 2002; Lin, Latchman, Katar &. Lee, 2003). The results show that PLC solution can outperform wireless networks in indoor.
HomePLug V1.0 for Vehicle: Experimental Results In (Gouret, Nouvel, El Zein, 2006; Gouret, Nouvel, & El Zein, 2007) the authors experiment two HomePlug V1.0 PLC modems inside the cars. The first, designed by LEA uses the standard Homeplug v1.0 and the Intellon chip (“LEA”, 2006), and the second from Spidcom uses an owner standard (“Spidcom”, 2008). In the Spidcom system, the OFDM modulation is based on 896-carriers from 0 to 30 MHz divided into 7 equal sub-bands. For the data transmission, BPSK up to 256-QPSK modulation is used in conjunction with error correction (Viterbi & Reed Solomon) and a bit-loading scheme. The MAC layer provides a mechanism based on TDMA (CSMA/CA also available) with a master/slave prioritisation. For each modem, coupling and filters have been optimized to fulfil the class 4 CISPR 25 with a typical limit of -35 dBm/Hz in the [0-30] MHz bandwidth. The testbed, illustrated in Figure 12, is constituted of a pair of identical PLC modems connected to PCs through Ethernet
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Figure 12. PLC Testbed for PLC
LAN and placed in different point of the vehicle (near trailer hitch, cockpit, engine and cigarette lighter). Each one is connected to the electrical 12V supply network with the coupler and the filter. A spectrum analyser and an oscilloscope are connected to the DC line through a filter that behaves as a high-pass filter with a 500 kHz cut-off frequency. The first path corresponds as a direct
way between the engine and the trunk. For the second, it corresponds also as a direct way, but between ECUs of the cockpit and the cigarette lighter. The throughput and transmitted power were measured. Only a summary of findings is presented here. The first results (Figure 13a and Figure 13b) show that the levels of broadcast power are differ-
Figure 13. Spectrum Measurement [0-30] MHz: a. HomePlug V1.0 b. Spidcom
a.
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b.
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ent for the two modems while keeping the power under EMC/CISPR25 requirements. Concerning the Spidcom one, its broadcast power is of the order of 5 dB lower than LEA. To be able to compare both modems, the bands 6 and 7 were switched off to obtain the same used bandwidth. Both modems transmit TCP or UDP data flow. Iperf tool (“Iperf”, 2008) is used for measuring maximum TCP and UDP bandwidth performance. Iperf allows the tuning of various parameters and UDP characteristics. Iperf reports bandwidth, delay jitter, and datagram loss. During the data rate measurement, the Spidcom modem transmitted up to 9 Mbps against 5.67 Mbps for the LEA. Spidcom modem integrates a pre-equalization function which improves the data rate by taking into account the SNR (signal-to-noise ratio) for modulation on each sub-carrier. When this function is involved, we obtain a data rate of 12 Mbps. Such results can be obtained by using up to 256-QPSK modulation on sub-carrier with high signal-to-noise ratio, not available on Homeplug chip which uses the same modulation for all the sub carriers. The best data rate obtained with the whole spectrum and optimization reaches 20 Mbps.
Discussion The results are promising and demonstrate the possibility of implementing a PLC inside vehicle based on a modified OFDM/HomePlug V1.0 while fulfilling vehicle requirements namely low cost, high data rate similar to Flexray and flexibility. As Spdicom modems can achieve 20Mbps using the 30 MHz bandwidth, it seems it can compete with Flexray. However, only no critical functions have been considered. Redundancy is only available through multiple transmissions as error correction and detection is applied in the receiver. Similar PLC tests have been carried out in (Degardin, Liénard, Degauque & Laly, 2007). However, to
improve data and bit error rate, additional work is necessary leading to the following questions: 1) What is about the throughput when more than two PLC nodes are transmitting? 2) What is about the real time performance of such implementation? 3) How does the PLC solution facilitate integration and diagnose? 4) How can it be interfaced with other wired protocols? To answer the questions above, the research has to be conducted by surveying the research community. We will now focus on wireless solutions and their viability for automotives.
WirELESS Ccommunication in automo During the last several years, the interest in wireless networking has grown significantly due to the availability of many wireless products, such as cell phones, wireless enabled mice, keyboards, modems, and many other products. The reason for the rapid growth of wireless technology is that it provides the users with additional convenience over the wired technology. In automotive systems, both within and between the vehicles, there are several applications pushing for the adoption of wireless communications. Looking at in-vehicle communications, more and more portable devices, e.g., mobile phones, laptop computers and DVD player can exploit the possibility of interconnection with the vehicle. Wireless communication could be an attractive solution to reduce the number of cables and disturbances in cars. To the best of our knowledge, there are not a lot of wireless experiments in intra-car communications. Below, we review potential wireless solutions and discuss them. Among them, we specifically focus on three techniques working in the 2.4 GHz band and allowing data rate from 250 kbps up to 50 Mbps, namely Bluetooth, IEEE 802.11 b/g and ZigBee.
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Bluetooth Wireless Technology Bluetooth (“Bluetooth”, 2008) operates in the 2.4GHz ISM (Industrial Scientific Medical band) band. It is a low-power, low-cost, short-range wireless communication system developed by a consortium of many manufacturers (Ericsson, IBM, Intel, Nokia and Toshiba, etc). Dividing the ISM band into 79 channels, the Bluetooth system uses the Frequency Hopping Spread Spectrum (FHSS) technology to operate on difference channels with the frequencies changing rate of 1600 times per second when connected and 3200 times per second when in inquiry and page mode. Bluetooth topology is made up of the piconets that contain a single master node and up to seven slave nodes. Communication among the slave nodes has to be performed from the master node. Low power is one of Bluetooth’s main advantages, from 100mW to 1mW, with a coverage range from 100 meters down to 1 meter. It provides speeds of up to 3 Mbps (Bluetooth V2.0). General Motors Corporation introduced a Bluetooth network in its 2003 Saab 9-3 model car. If the Bluetooth technology is used to establish the wireless connection among the nodes in the CAN or LIN bus, the volume of wires may decrease. Bluetooth wireless connection can supply enough transmission rate for the control network. Indeed, the asynchronous data transmission rate in the Bluetooth network is up to 723.2Kbps asymmetric (433.9Kbsps symmetric). While in CAN, the data transmission rate for the real time control sub network is typically > 250Kbps, and about 125Kbps for the information sharing sub network. As demonstrated in (Chen & Chen, 2005), though an in-vehicle wireless network can provide the users with many convenient features, it can also make the vehicle system vulnerable to many types of security attacks and also has to face some problems. As interference and setup delay time is very dangerous for the vehicle’s safety, right now
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the Bluetooth wireless technology is not proper to be used in X-by-wire systems. However, it can be used in the nonsafety-critical control systems. It is estimated that diagnosis process may become the first practical Bluetooth system level application in the vehicles (Leen, Hefferman, 2001). Other studies have been carried out in order to build a secure Bluetooth network (Mahmud & Shanker, 2006).
IEEE 802.11 B/G Wireless Technology: Wi-Fi IEEE 802.11 (Wi-Fi is in current usage) is a set of standards for wireless local area network (WLAN) computer communication, developed by the IEEE LAN/MAN Standards Committee (IEEE 802) in the 2.4 GHz, 5 GHz and public spectrum bands. It includes IEEE 802.11b/g standards in the 2.4 GHz band and IEEE 802.11a in the 5 GHz band (“802.11”; Gast, 2002). IEEE 802.11b is a based on the direct-sequence spread spectrum modulation technique. Technically, IEEE 802.11b applies Complementary code keying (CCK) as its modulation technique. As it uses the CSMA/CA media access method, the maximum IEEE 802.11b throughput that an application can achieve is about 5.9 Mbit/s using TCP (7.1 Mbit/s using UDP). Then, the IEEE 802.11g was the third modulation standard for Wireless LAN. The modulation scheme used in 802.11g is OFDM. Like IEEE 802.11b it works in the 2.4 GHz band but operates at a maximum raw data rate of 54 Mbit/s (19 Mbit/s net throughputs). 802.11g hardware is fully backwards compatible with 802.11b hardware. For the two standards, two network topologies are available namely ad-hoc or infrastructure. A wireless ad hoc network is a decentralized wireless network where each node is willing to forward data for other nodes, and so the determination of which nodes forward data is made dynamically based on the network connectivity. On the other hand, in the infrastructure
Automotive Network Architecture for ECUs Communications
network, a wireless access point connects wireless communication devices together to form a wireless network. Data rate measurements have been carried out between two ECU/modems according to the engine and equipments states using the infrastructure configuration. The test bed is built up of two 802.11g modems connected to laptop for IP transfers. They are placed as for PLC cases. The authors observe that the data rate decreases less than in the PLC configuration according to the engine states and equipments. We can achieve about 12 Mbps. The worst configuration is when the transmitter is placed in the trunk and the received antenna is placed near the dash-board. Many reflections appear and the signal is altered. As for Bluetooth, IEEE 802.11 b/g can be proposed as a potential wireless solution. However to our knowledge there are not a lot of experimentations of 802.11 b/g in vehicles. Besides, IEEE 802.11 b/g devices suffer interference from other products operating in the 2.4 GHz. When 802.11b/g is used in a point-to-multipoint configuration, the overall bandwidth is dynamically demand shared across all the users on a channel. Thus, real time is no more guaranteed. In (Lin, Haniph, Latcman & Neuman, 2003), the authors have conducted field test in houses in order to compare the performances of IEEE 802.11 in infrastructure mode and HomePlug V1.0. Field tests show that HomePlug had a better coverage than IEEE 802.11. However, IEEE 802.11 was nearly always very high (above 4 Mbits/s in 802.11b mode in 70% of connections) but decreases more rapidly (less than 3 Mbits/s) than HomePlug (above 4Mbits/s in 58% of connections) when distances are larger than 20 meters. In fact, the Homeplug performance is not correlated with the line sight distances measured in the experiment as the signals have to pass through power lines cables. In (Lee, Latchman, Newman, Katar & Yonge, 2002) the authors have carried out similar experiments. This study compared two technologies for implementing a small of-
fice network without extra cable installation. The overall results show that the power line network outperforms the wireless network. It is not possible to directly apply the results obtained for indoor power lines to automobiles because the environment and topologies are totally different. Thus, additional measurements must be carried out to investigate these wireless network solutions.
ZigBe Wireless Technology An additional intra-car wireless sensor network is based on ZigBee, which offers lower data rate but can be used for many embedded applications. It can be a good alternative to LIN network. ZigBee is an industry alliance that promotes a set of rules which builds on top of the IEEE 802.15.4 standards (“ZigBee”; Tsai, Saraydar, Talty, Ames, MacDonald, & Tonguz, 2007). ZigBee is a low-cost, low-power, wireless mesh networking standard. It operates in three ISM radio bands: 868 MHz in Europe, 915 MHz in countries such as USA and Australia, and 2.4 GHz (16 channels) in most jurisdictions worldwide. The modulation uses direct-sequence spread spectrum coding, which is managed by the digital stream into the modulator. BPSK and QPSK could be used in the 2.4 GHz band. The data rate is 250 Kbps per channel in the 2.4 GHz band (40 Kbps in the 915 MHz band and 20 Kbps in the 868 MHz band). Transmission range is between 10 and 75 meters, although it is heavily dependent on the particular environment. The maximum output power of the radios is generally 0 dBm (1 mW). CSMA/CA is the usually channel access mode, except three cases (beacons, acknowledgment, messages for beacon oriented network devices). As ZigBee provides network speeds of up to 250 Kbps, it is expected to be largely used as a sensor network for monitoring and control purposes (air conditioning, heating, ventilation, lighting control, etc.).
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In the article (Tsai, Saraydar, Talty, Ames, MacDonald, & Tonguz, 2007), the channel behaviour under various scenarios is observed for ZigBee nodes placed throughout a midsize sedan. Results indicate that the change in link quality with respect to locations of nodes in the car is significant. Engine noise can increase the Packet Reception Rate throughput received sensitivity threshold by 2 to 4 dB. In general, the experiments and measured results indicate that ZigBee is a viable and promising technology for implementing an intra-car wireless sensor network. However, the communication between sensor nodes and a base station in the car will depend on several factors, such as the power loss, coherence bandwidth, and coherence time of the underlying communication channels between the sensor nodes and the base station. Other high data rate technologies are available such as IEEE 802.15.3a (UWB) which is a competitor to the other IEEE 802.11 solutions. It can provide speeds of up to hundred of Mbps/s. It can be likely used in multimedia applications. However, UWB components and boards are not actually still available.
Wireless Technologies Comparison and Design for Automotives Todays, several applications push for the adoption of intra-car wireless applications. Considering voice and multimedia applications up to sensor/ controls applications, the technologies are already available. The main differences between the wireless technologies we have considered come from the different target applications they are designed for. Bluetooth can be suitable for voice and audio applications thus eliminating short distance cabling. It can address small file transfer between devices in a network range of a few tens of meters and with network speeds of a few (1-3) Mbps. IEEE 802.11 b/g can address audio, multi-media applications as file transfer with much higher throughput. Plug and play devices like personal
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digital assistants, DVD players can communicate with each other while they could also connect with the other nodes already installed in the car to provide a multifunctional device interface. Documents from one PC could be displayed on LCD screens installed in different places of the vehicle (dashboard, back of seat). This solution can compete with MOST and IDB1394 whose main disadvantage is their dedication to a specific application. Another potential use is inter-vehicle communications as it consumes high power. ZigBee, on the other hand, addresses sensors and control. ZigBee applications are consisting of lots of devices typically requiring small data packets. It can provide speeds of 250Kbps which is not so important for non-critical and safety applications. Nodes can be scattered around in a slightly larger area compared with Bluetooth. Bluetooth can claim to replace some CAN’s applications as ZigBee replaces some LIN functions. But actually, no wireless network can answer X-by-wire requirements allowing potential Flexray replacement. If we consider power usage and real time constraints, both ZigBee and Bluetooth require much less power than IEEE 802.11 b/g whereas low power is a strong cars manufacturer’s requirement. From a real time point of view, multi-media applications do not require strictly real time. On the other-hand, safety-systems have real time requirements, e.g., collision detection/avoidance systems or active suspension systems that respond to road conditions. Moreover, diagnostics tools can make real time data available during operation of the vehicle. Also, wireless networks can operate as a redundant link between nodes linked with wired different networks for real time applications. Furthermore, in order to study these alternative solutions, wireless channel measurements must be carried out, considering different experimental scenarios (engine ON/OFF, driving, passengers present or not, driver always present, equipment ON/OFF, …).
Automotive Network Architecture for ECUs Communications
Conclu The partitioning of automotive applications into several functional domains with their own requirements can be useful to manage complexity but it leads to a great number of several independent networks whereas more and more cross domain data exchanges are necessary. Then, gateways must be implemented and can impact performances in term of real time. Such example of cross-domain can be cited as the engine controller (powertrain domain to body domain) or upcoming X-by-wire functions. An initial foreseeable solution with PLC has been presented to improve interoperability between applications with different requirements. PLC network answers both to cost, flexibility and throughput requirements. Future work should be devoted to optimize power line architectures in order to minimize the number of cables, splicing, etc. This implies rethinking the DC bundles as rethinking the implementation of networks as independent domain. Additionally, the technical solution must be optimized taking into account more channel measurements. Wireless solutions can be adopted inside automotive applications as more and more devices are both used in home and in cars while operating as non-critical application or as redundant links. Studies should be devoted to test these wireless systems in intra-car communications while optimizing them to fulfil flexibility, fault tolerance, security, real time and EMC requirements. In 2003, AUTOSAR (AUTomotive Open System Architecture) (Fennel & Bunzel, 2006) has been jointly developed by automobile manufacturers, suppliers and tool developers. It is defined as an open and standardized automotive software architecture. AUTOSAR aims as minimizing the barriers between functional domains while facilitating the re-use of soft and hardware components between different vehicle platforms. To achieve this, AUTOSAR defines a methodology that supports a distributed, function-driven development
process and standardizes the software-architecture for each ECU in such a system. AUTOSAR also specifies compatible software-interfaces at application-level. Applying AUTOSAR will improve the interoperability between applications and sub-networks. The automotive industry has recognized that a technological breakthrough is required and the development and standardization of AUTOSAR must be handled by many companies. Today, LIN, CAN and MOST are AUTOSAR compliant. In its concept, AUTOSAR introduces a Virtual Functional bus (VFB) which separates applications from infrastructure (i.e ECU and networks). An application consists of interconnected “AUTOSAR Software Components”. The VFB provides standardized communication mechanisms and services for the components. It acts independently from the chosen mapping of these components to the infrastructure of the interconnected ECUs. Taking into account AUTOSAR concept, if the proposed networks (PLC or wireless) are introduced in vehicles, they must be adapted and must demonstrate their conformance to the AUTOSAR standard.
REFERENCES 802.11 (1999). Technical Specification. Retrieved from ref http://standards.ieee.org/getieee802/ download. Afkhamie, K. H., Katar, S., Yonge, L., & Newman, R. A. (2005). Overview of the upcoming HomePlug AV Standard. Power Line Communications and Its Applications, International Symposium, 6-8 April, (pp. 400- 404). Automotive Systems Research Group. (2008). Retrieved from http://www.ttcan.com/ASRG Beikirch, H., & Voss, M. (2000). CAN-transceiver for field-bus power line communication. ISPLC Symposium, Limerick, Ireland, April, (pp. 257–264). 87
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Bluetooth (2008). Retrieved from http://www. bluetooth.org/ CAN. (2001). Retrieved from official web site http://www.can-cia.org/ Chen, Y., & Chen, L. (2005). Using Bluetooth wireless technology in vehicles. IEEE International Conference Vehicular Electronics and Safety, 14-16 October, (pp. 344- 347). CISPR25. (2002). Limits and methods of measurement of radio disturbance characteristics for the protection of receivers used on board vehicles. Technical report, CISPR-25, 2002. Degardin, V., Laly, P., Liénard, M., & Degauque, P. (2006). Impulsive Noise on In-Vehicle Power Lines: Characterization and Impact on Communication Performance. IEEE International Power line Communications and Its Applications Conference, ISPLC 2006, 26-29 March, (pp. 222-226). Degardin, V., Laly, P., Liénard, M., & Degauque, P. (2007). Performances of the HomePlug PHY layer in the context of in-vehicle powerline communications. IEEE International Power line Communications and Its Applications Conference, ISPLC 2007, 26-28 March, (pp. 93-97). Fennel, H., & Bunzel, S. (2006). Achievements and exploitation of the AUTOSAR development partnership, Copyright © 2006 CTEA. Retrieved from official web site http:www.autosar.org FlexRay Consortium. (2004). FlexRay Communication System, Protocol Specification, Version 2.0. Retrieved from: http://www.flexray.com Gast, M. S. (2002). 802.11 Wireless Networks, the definitive guide. O’Reilly Publishing. Gavette, S., & other HomePlug members HomePlug Powerline Alliance (2006). HomePlug AV Technology Overview. Technical report. Sharp Laboratories of America. Retrieved from download.microsoft.com/download.
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Gouret, W., Nouvel, F., & EL Zein, G. (2006). Additional Network Using Automotive Powerline Communication. IEEE International Power line Communications and Its Applications Conference, ISPLC 2006, 26-29 March, (pp 1087-1092). Gouret, W., Nouvel, F., & EL Zein, G. (2007). High Data Rate Network Using Automotive Power Line. IEEE International Power line Communications and Its Applications Conference, ISPLC 2007, 26-28 March. (pp. 1-4). Hara, S., & Prasad, R. (1997). Overview of multicarrier CDMA. IEEE Communications Magazine, December, (pp. 126-133). Heiskala, J., & Terry, J. (2002). OFDM wireless LANS: A Theorethical and Practical guide. Sams Publishing. Home Plug V1.0 (2008). HomePlug Powerline Alliance. Retrieved from official web site www. homeplug.org. Huck, T., Schirmer, J., & Dostert, K. (2005). Tutorial about the implementation of a vehicular high speed communication system. IEEE ISPLC. IDB Forum. (2008). Retrieved from web site http://www.idbforum.org. IDB1394, (2008). 1394 Automotive Presentation, Max Bassler, Shenzen, China, 10 April 2008. Retrieved from web site http://www.idbforum.org IEEE 1901 (2008). Retrieved from official web http://grouper.ieee.org/groups/1901/Scope_Purpose.html Iperf, (2008). Iperf tool and documentation, Retrieved from official web http://sourceforge. net/projects/iperf. J2056/2 Survey. (1994). J2056/2 survey of known protocols. In SAE Handbook. Warrendale, PA: Soc. Automotive Eng. (SAE), vol. 2. Johansson, K., Törngren, M., & Nielsen, L. (2005), Handbook of Networked and Embedded Control
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Systems, D. Hristu-Varsakelis and W. S. Levine, Eds. Boston, MA: Birkhäuser, 2005. Kopetz, H., et al. (2002). Specification of the TTP/A Protocol. (Technical Report). Vienna, Austria, University. Technology. LEA. (2008). LEA Organisation. Retrieved from official web site http://www.leacom.fr. Lee, M. K., Latchman, H. A., Newman, R. E., Katar, S., & Yonge, L. (2002). Field performance comparison of IEEE 802.11b and HomePlug 1.0 . IEEE Conference Conference Local Computer Networks LCN 2002. 27th Annual (pp. 598- 599). Leen, G., & Hefferman, D. (2001). Vehicles without wires. Computing & Control Engineering Journal, 12(5), 205-21. Lienard, M., Carrion, M., Degardin, V., & Degauque, P. (2008). Modeling and Analysis on In-vehicle power line communication channels. IEEE Transaction Vehicular TEchnologie, 57(2), 670-679. Lin, Y., Latchman, H., Katar, S., & Lee, M. K. (2003, May). Theoretical and Field Performance Comparison between 802.11 Wireless and Powerline HomePlug 1.0 Protocols. IEEE Communications Magazine with Focus Theme on Powerline Local Area Networks, (pp. 54-63). Lin, Y. J., Haniph, A., Latchman, H., Neuman, R. (2003, April). A comparative Performance study of Wireless and Power Line Network. IEEE Communications Magazine, (pp. 54-63). LIN Consortium. (2003). LIN Specification Package. Revision 2.0.Retrieved from http://www. lin~subbus.org/. Mahmud, S., & Shanker, S. (2006, May). In-Vehicle Secure Wireless Personal Area Network (SWPAN). IEEE Transactions On Vehicular Technology, 55(3), 1051-1061.
MOST (2006). MOST Core Compliance Test Specification V2.00. Technical Report. Retrieved, from http://www.mostcooperation.com/publications. MOST Cooperation (2008). MOST Specification Revision. Retrieved from http://www.mostnet. de. Navet (2008). The Automotive Embedded Systems Handbook. Industrial Information Technology series, CRC Press / Taylor and Francis, ISBN 978-0849380266, to appear November 2008. Navet, N., Song, Y-Q., Simonot-Lion, F., & Wilvert, C. (2005, June). Trends in Automotive Communication Systems. Proceedings of the IEEE, special issue on Industrial Communications Systems., 93(6), 1204-1223. Nolte, T. (2003). Reducing Pessimism and Increasing Flexibility in the Controller Area Network. Mälardalen University Licentiate Thesis, May 2003 Department of Computer Science and Engineering Mälardalen University Västeras, Sweden, Copyright Thomas Nolte, Printed by Arkitektkopia, Västeras, Sweden Distribution . Nolte, T. (2006). Share-Driven Scheduling of Embedded Networks. Mälardalen University, Dissertation, May 2006. Department of Computer Science and Engineering Mälardalen University Västeras, Sweden, Printed by Arkitektkopia, Västeras, Sweden Distribution. Nouvel, F., El Zein, G., & Citerne, J. (1994, June). Code division multiple access for an automotive area network over power-lines. Proceedings of IEEE Vehicular Technology Conf., (pp. 525–529). Road Vehicles. (2004). Controller Area Network (CAN)—Part 4: Time-Triggered Communication, ISO 11 898-4. Rubin, A. (2002). Implementing automotive protocols for communications over noisy battery
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power lines. Proceedings of the IEEE Conference on Conv. Elect Electron. Eng. Israel, December, (p. 306). Schiffer, A. (2000). Statistical channel and noise modelling of vehicular dc-lines for data communication. Proceedings of IEEE Vehicular Technology Conf.,Tokyo. Spdicom (2008). Spdicom Inc., Retrieved from official web Official web site http://www.spidcom.com. Tsai, H., Saraydar, C., Talty, T., Ames, M., Macdonald, A., & Tonguz, O. (2007). ZigBee-based Intra-car Wireless Sensor Network, Communications. IEEE International Conference ICC’07, 24-28 June, (pp. 3965-3971). Valéo (2006). Valéo Inc., Electrical and Electronic Distribution Systems: Focus on Power Line Communication. Retrieved from official web http://www.valeo.com/ . ZigBee (2000). ZigBee Specification. Version 1.0, December. Retrieved from http://www.zigbee. org;
KEY TERMS Bluetooth: Wireless master-slave network in the 2.4 GHz band
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Control Area Network (CAN): Protocol network CSMA/CA: Carrier sense multiple access with collision avoidance ECU: Electronic Control Units Flexray: protocol network HomePlug: Power line communication network IDB 1394: Intelligent transportation systems data bus using IEEE1394 technology—protocol network Local Interconnect Network(LIN): Protocol network Media Oriented Systems Transport (MOST): Protocol network OFDM: Orthogonal frequency division multiplexing PLC: Power line communication TDMA: Time division multiple access IEEE 802.11 b/g: Wireless local area network in the 2.4 GHz band (Wi-Fi) ZigBee: Wireless sensor protocol network in the 2.4 GHz band
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Chapter V
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control Lek Heng Ngoh Institute for Infocomm Research, A*STAR, Singapore
ABSRACT The use of in-car networked electronic controller units (ECUs) for monitoring and control of various vehicle subsystems has become a common practice among the automotive manufacturers. In this chapter, the author surveys one of the most popular in-car networking technologies, the Controller Area Network (CAN), as well as newer and emerging in-car network technologies called FlexRay and Media-Oriented System Transport (MOST). Currently, these networks are deployed for in-car applications such as engine diagnostics, and infotainment systems. In this chapter, however, the author extends the use of these embedded vehicular networks by proposing to remotely monitor and control the vehicles through them, in order to realize safety and driver assistance related applications. To accomplish this task, additional technologies such as real-time wireless communications and data security are required, and each of them is introduced and described in this chapter.
Introduction In this chapter we present the increasingly important technological advances in a networked vehicle and the related technologies in achieving secure wireless real-time monitoring and control of these vehicles. As more electronics found their way into the vehicle, the ability to remotely gather
and send information from and to the on-road vehicles opens up many application opportunities in the effort to make future vehicles safer (both the drivers and road users), more economical, more fun (e.g. multimedia entertainment) and last but not least, to simplify the often mammoth tasks of traffic management and road maintenance. However, for this vision to become a reality technology
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Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
development must take place on several fronts. This chapter attempts to present an overview and analysis of these key technical components. The chapter is organized as follows. We begin by elaborating the controller area network (CAN) standard which is the most widely deployed invehicle network system. Our description includes an overview of the CAN technology (Section III), its deployment in a vehicle (Sections IV and V). The above is followed by a description of the various CAN-base car telematic applications (Section VI). A brief introduction is given in Section VII on other networking standards that are currently deployed in vehicles. In Sections VIII and IX, we elaborate two key components in achieving remote vehicle monitoring and control, namely the wireless communication component and data security component. In Section X, we present an overall architecture of the secure wireless realtime vehicle monitoring and control environment. Finally, in Sections XI and XII, we discuss some possible future trends in this field and conclude this chapter.
II. Background The Controller Area Network (CAN) (CiA, 2008; Audi CAN-bus, 1998) is a serial bus communications protocol developed by Bosch in around1983. CAN is a standard suitable for efficient and reliable communication between sensors, actuators,
controller, and other nodes in real-time applications. Since then, CAN has evolved to become the de facto low-cost standard in a large variety of networked embedded control systems. Many of the early deployments of CAN are found in automotive vehicles. To-date, however, CAN has been used in a variety of application domains such as transportation (e.g. trains), manufacturing (e.g. process automation), construction (e.g. structural monitoring), agriculture (e.g. agriculture equipment), healthcare (e.g. medical devices), entertainment (e.g. arcade game machine), and even in the control of scientific apparatus (e.g. proton accelerators). In this chapter, however, we will only focus on applications of CAN within the automotive vehicles (see Figure 1). In 2007, it is estimated that sale of vehicular CAN nodes has reached more than 600 million each year (Johansson, K.H., Törngren M., & Nielsen L., 2005). Looking ahead, the number of CAN nodes deployed in automotive vehicles is expected to increase substantially as by 2008, all new vehicles sold in the US will be required to be CAN-compliant, in particular, all new vehicles are to equip with CAN-based diagnostic system. Moving forward, the number of automotive manufacturers adopting CAN for in-vehicle embedded data communications and control is expected to increase as more Japanese models have started to adopt CAN-based technologies which are relatively low-cost and a well-established standard.
Figure 1. CAN Replaces traditional mesh wiring in a vehicle device 1
device 1
ECU
Without CAN Bus
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device 2
ECU
With CAN Bus
device 2
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
In the rest of this chapter, we will discuss the various system components involved to accomplish the goal of monitor and control vehicle remotely and securely. We first elaborate the basis and the general technology landscape of CAN deployments in automotive industry; followed by an overview of the deployment of in-car CAN networks. Next we discuss the various existing and future applications that make use of the CAN data. The two key technologies components needed to achieve secure remote vehicle monitoring, namely data security and wireless communications are discussed toward the end of the chapter.
III. OVEF CAN TECHNOLOGIES An overview of CAN technologies is presented in this section. CAN is a twisted-pair serial lines with realtime communications protocol particularly suited for connecting sensors (for monitoring purposes), and actuators (for control purposes) which are commonly found in today’s vehicles – a high-end passenger car today can have up to hundreds of sensors and actuators connected by 80 electronics control units (ECUs) (Audi CAN-bus, 1998) which form the CAN nodes (see Figure 1). Details of the CAN bus operations are described in many literatures elsewhere. An overview is provided here and interested readers are referred to these literatures for further information (CiA, 2008). All CAN nodes are connected to the CAN bus in parallel, and communicate with each other via a data frame which forms part of the CAN standard (see Figure 2 and Figure 3). A data frame begins with a single start-of-frame (SOF) bit, followed by eleven bits of identifier (ID) (there is also an extended data frame with twenty-nine bit identifier), and the remote transmission request (RTR) bit. The ID and RTR bits form the arbitration field (see Figure 3). The control field consists of six bits, and indicates how many bytes of data there
are in the data field – which can be zero to eight bytes long. The data field is followed by the cyclic redundancy checksum (CRC), which allows the receiver to check if the data has been corrupted during transmission. Next the two-bit acknowledgement (ACK) field is for any of the receiver to indicate to the transmitter that it has received a valid frame. Finally, the end of message is signally through a seven-bit end-of-frame (EOF). We shall describe the transmission process next. As all CAN nodes are allow to transmit data onto the CAN bus any time, therefore it belongs to the class of protocols known as carrier sense multiple access/collision avoidance (CSMA/CA). In order to provide the necessary arbitration among the conflicting CAN nodes, it uses a non-destructive arbitration process. This process requires the transmitting nodes to listen to the network in order to avoid collisions. This is how it works: whenever the CAN bus is free, any ECU can start to transmit a message, any possible conflicts due to more than one ECU transmitting simultaneously are resolved by bit-wise arbitration using the identifier of each message. During arbitration, each ECU transmits its message identifier and compares it with what level it “hears” from the network. If it wins in the bit-wise arbitration, the transmission process for this ECU continues, otherwise it quits transmitting. Using this process, CAN bus guarantees that there will be only one transmitter left transmitting at the end of the arbitration phase, with the rest of the ECU revert to receiving (or listening) mode. For cost control reasons, CAN bus comes in different speed ratings depending on the functions that it is deployed to control. Currently there are three classes of CAN bus (Class A to Class C). Class A speed rating is the slowest, typically around 10 kbps (thousand bits per second). It is typically deployed to control components such as power mirror, power windows, door locks and lights. Class B CAN, by comparison, may operate for 10 kbps to 125 kbps. It is fast enough to carry more complex information and time-sensitive
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data such as electronic instruments, electronic transmission controls, climate controls. Finally, Class C CAN bus is the fastest with bit rate up to 1 Mbps. Many of the vehicles that are currently using a Class C data bus are operating at speeds of around 500kbps. At this rate, it is fast enough for power-train control modules, air bag modules, and fast acting anti-lock break and stability control system. The development of CAN continues, and it can be expected that faster versions of CAN bus will make their way into a shop near you soon. The CAN protocol defines the lowest two layers (i.e. physical and data-link layer) of the Open System Interconnection model i.e. OSI’s
7-layer model, in order to meet certain application requirements, higher layer protocols have also been developed to be added to the current two-layer specification. Many of these higher layer protocols were developed for applications outside of automotive industry (e.g. distributed industrial automation) (Kvaser, 2008; AALCAR, 2008). However, these protocols have now become relevant as we explore transmitting of CAN bus data remotely (see Section VIII). An example of a higher layer protocol is the CANopen (Kvaser, 2008) which is a standardized layer defined for distributed industrial automation. It specifies communication profiles and device
Figure 2. Parallel ECU connections to CAN bus Sensors/Actuators
Sensors/Actuators
Electronics Control Unit (ECU)
Electronics Control Unit (ECU)
CAN Bus
CAN Bus
Figure 3. Details of CAN message data frame Arbitration Field (12 bits/30 bits)
Start field (1 bit)
RTR (1 bit)
Message ID (11 bits/29 bits)
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Control field (6 bits)
CRC (16 bits) Data field (max 64 bits)
ACK field (2 bits)
End field (7 bits)
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
profiles, which enable an application-independent use of CAN. In this case device profiles are defined for most common devices such as analogue-to-digital converters. Furthermore, CANopen also distinguishes real-time data verses less time-sensitive data. Other standardized higher layer protocols include DeviceNet (Kvaser, 2008) (adding Internet protocols TCP/IP on top of CAN). Another higher layer protocol is CAN Kingdom (Kvaser, 2008). CAN Kingdom was developed mainly for maritime industry. Its main feature is that it allows changing of CAN network behavior at any time, including whilst in run-time. CAN Kingdom allows for system troubleshooters to turn off individual nodes. It will also allow for dynamic and run-time change of node identifier and conditions for sending messages. Finally, new developments have also taken place for basic CAN protocols. The latest extension of CAN includes the capabilities such as Time-Trigger communication on CAN (TTCAN, (TTCAN, 2008)) verses the traditional event-triggered CAN. TTCAN allows the nodes to have guaranteed access to the communication bus with a fixed periodic interval.
Figure 4. An example CAN deployment in car
IV. EXAMPLE DEPLOYMENT OF CAN NETWORK IN VEHICLES
Table 2. Examples of CAN ECU for convenience subsystems
In the automotive industry, there has been a remarkable evolution over the last few years in which embedded control systems have grown from standalone control systems to highly integrated and networked control systems. Many car manufactures today design their control architectures based on CAN, including BMW, Chevrolet, Fiat, Ford, Mazda, Mercedes, Renault, SAAB, Toyota, Volkswagen, and Volvo. Typical CAN-based distributed control architecture would consist of multiple CAN networks of ECUs namely power-train and chassis subsystem, infotainment subsystem, and body electronics subsystem (see Figure 4). Tables 1-2 show the details of these three CAN networks.
Headlamp Wipers Power train
CAN ECU CAN Bus
door
Audio speakers
Table 1. Examples of CAN ECU for powertrain and chassis subsystems Action
Data Speed
Engine control
Monitor/Control
500kps
Gearbox control
Monitor/Contro
500kps
Gear selector lever
Monitor/Contro
500kps
Break control unit
Monitor/Contro
500kps
Action
Data Speed
Left turn signal
Monitor
125 kbps
Right turn signal
Monitor
125 kbps
High Beam
Monitor
125 kbps
Wiper Hi
Monitor
125 kbps
Wiper Lo
Monitor
125 kbps
Wiper int. approx 17 seconds interval
Monitor
125 kbps
Wiper int. approx 15 seconds interval
Monitor
125 kbps
Head light “on” Lo
Monitor
125 kbps
Head light “on” Hi
Monitor
125 kbps
Reverse light “on”
Monitor
125 kbps
Brake light “on”
Monitor
125 kbps
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V. MANUFACTUERE -SPECIFIC CAN AND LoCATION DATA Even though CAN bus is a well-established standard for intra-car data communications, car manufactures have taken the liberty to implement and deploy the network freely. The reality is that there is no standard of allocating the data bits with respect to the various CAN sub-systems. Each car manufacturer has therefore assigned and defined their own CAN bit pattern for respective CAN node. This vendor-specific information must therefore be obtained in order for any software to decode and make use of the CAN data. Furthermore, most vehicles today are equipped with Global Positioning System (GPS) receivers where the location information of the vehicles is constantly obtained. As we will elaborate in the next section, location information for each vehicle is crucial to many of the vehicle telematic applications, as well as future multi-hop car-to-car communications, and should therefore be handled and communicated along with the CAN bus data described in this section.
the transmission and use of this data externally from one or more vehicles are yet to be exploited for a wide range of applications, some of which are even beyond our current imagination. As described in previous section, CAN bus has been used for not just vehicle’s power-train and chassis, it has also been deployed at both the comfort and convenience (e.g. seat, parking aid, door, interior climate control), as well as infotainment (radio, phone, navigation) subsystems. All these mean that with the aplenty CAN basic data, a host of remote applications can be developed to cater for all aspects of need of the drivers and their vehicles (Jaman G., 2007). In the rest of this section, some of these futuristic applications will be described at conceptual level to give readers a sense of the emerging applications at the horizon. 1.
VI. VEHICLE APPLICATIONS USING CAN BUS DATA Today the adoption of CAN bus is not only motivated by saving and simplifying cabling as was the case in the beginning. A number of applications such as diagnostic and engine control functions have been developed. Beyond these, some highly integrated and networked control systems that involve distributed coordination of many sub-systems are also been developed. In many exiting and future vehicles, information of a car’s multiple ECUs will be shared and exchanged over the CAN bus. Examples of use of this data include vehicle diagnostics, dynamics control and even drive-by-wire systems (e.g. brake-bywire, steer-by-wire etc.). Whilst up to now data is only available and used within the vehicle,
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2.
Maintenance and diagnosis: Based on the above description of CAN operations thus far, it should not be difficult to conclude that in order to read the data on CAN bus, one has to do so through a device mimicking the operations of a “receiver-only” ECU node. Therefore, CAN-compliant SCAN tools (AALCAR, 2008) are currently available commercially to read the vehicle CAN data serially. All CAN ECU modules can send and receive “I’m OK” signals to inform each other their status. On the other hand, whenever a problem occurs in one of the modules, it will not generate the “ok” signal and this in turn will be detected by the SCAN tool and inform the user. A faulty ECU module in this case may not start a vehicle, and hence the follow-up action is for the vehicle to be checked and the faulty ECU replaced at a workshop nearby. Similarly, making the CAN data remotely will enable such condition to be detected in advance and preventative maintenance can be carried out regularly. Safety: CAN networks have played a significant role in providing extra safety
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
3.
4.
features to the driver. Case in point is the electronic stability control (ESC) that is fast becoming a standard feature in even mid-range passenger vehicles. Essentially the ESC systems can detect unstable driving situation by comparing the driver’s intended course with the vehicle’s actual moment using CAN ECU data such as wheel speed, angle, yaw rate and lateral acceleration, and take necessary control action to stabilize the car and keep it on the road. Control actions of ESC include the use of a combination of anti-lock brake (ABS) and electronic force distribution etc. With more sophisticated sensory technologies (e.g. radar and video), future ESC systems will be able to detect signal such as driver’s physical conditions (e.g. drowsiness) and take appropriate corrective action such as to take-over control of the vehicle and park it somewhere safe! All the monitoring and actuation are communicated to the respective ECU via CAN networks. Traf. c telemetric: The use of on-the-road vehicles as speed sensors and to rely the information to a centralized server, where information is gathered and learned about the traffic condition, about a stretch of a road, have proven to be feasible (Johansson, K.H., Törngren M., & Nielsen L., 2005). With CAN bus information made available, collective information regarding the road conditions and even weather condition (via information on the use of headlights and wipers) can be obtained. A host of vehicular applications and services can then be developed to make use of this information. Driver assistance: Some of the up-and-coming driver assistive functions for automotive are the adaptive cruise control (ACC) and parking assistance. ACC’s main function is to adapt vehicle speed with respect to the information such as distance to the front car, obstacles and even weather (e.g. fog)
conditions. Similar to the sensor modalities used for providing safety features such as ESC, ACC and parking assistance systems will make use of the information collected through CAN networks, as well as using the CAN network to communicate commands to both the driver and relevant electronic systems Next, automatic parking assistance systems would probably rely on ultrasonic sensors mounted on the sides of the vehicle to measure available parking space length and depth. CAN bus would be used by the system to communicate commands to the subsystems such as steering control and brakes. Similar to the above cases, with remote monitoring and control, such features can be provided cheaper and earlier as many of these systems would require large computing resources (e.g. CPU power) which may not be readily available in a car. 5.
Infotainment-on-the-go: With the proliferation of personal entertainment devices and the availability of intelligent multipurpose monitor screens and audio devices in passenger cars, it is only a matter of time before the drivers are able to download infotainment content such as popular songs and surf the net on-the-go. CAN bus is already expected to play a significant role in the control and configuration of these in-car infotainment devices to facilitate these applications.
Clearly business aspects such as market introduction and key success factors need to be considered together as the related technologies are being overcome. It is worth noting that the relevant business issues here are no different from any Internet-based applications and services which we are familiar. However, one key important difference is that the availability (i.e. adoption rate) of car communication will greatly
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influence the kinds of applications that are likely to be introduced first. The two key factors are, the different data quality of service (QOS) that each mode of communication can deliver (e.g. data timeliness and reliability), and the availability of cars equipped with each mode of communication. Thus for example, car-to-car communications can support low-data latency but it is not expected to be widely available over-night, and therefore will limit the class of applications (e.g. lane merging) that would only work well if large percentage of vehicles are equipped with the capability to detect and transmit data in a car-to-car manner.
VII. OTHER IN-VEHICLE NETWORKING STANDARDS Whilst CAN is the most commonly found in-vehicle network today, there are other network systems which are being deployed to either inter-work or supersede CAN. An example of the inter-working network is the Local Interconnect Network (LIN) (LIN, 2008). LIN standard precedes CAN and is a single-wire bus system. A LIN network is typically made up of a master node and one or more (up to 16) slave nodes connected serially to the master node. A slave node can typically be a sensor such as a tilt sensor or anti-theft alarm sounder. It can also be an actuator in the form of an intelligent electronic or electromechanical assembly which comes under the control of a master node. The master node controls the speed (normally 1 to 20 kbits/sec), sequence, as well as the frequency of data transfer between itself and slaves nodes. A number of LIN master nodes, in turn, can be connected to a CAN ECU node. Next, with increasingly sophisticated electronics being added to the vehicles, a greater demand is placed on the interconnecting network bus in relation to data speed, time-determination and fault tolerance. FlexRay (FlexRay, 2008) is a
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new generation standard that has been designed to meet these requirements better than CAN. For the start, FlexRay is capable of delivering data rate of 10 Mbits/sec that is almost 10 times more than the fastest CAN available. FlexRay protocols also provide better timing requirement by transmitting messages in fixed time slots (verses the asynchronous CAN), and superior fault-tolerance and redundant message transmission on two separate physical channels. Unfortunately, FlexRay had not been designed to inter-work with CAN network in mind, and therefore a complete replacement of CAN is unlikely to happen anytime soon. Finally, an emerging future vehicular network standard that is gaining acceptance is the Media Oriented Systems Transport (MOST) (MOST, 2008) networking standard. Given the expected rise in in-car telematics and entertainment systems such as digitized audio, video contents, location-based applications and broadband Internet connections, a much higher data transfer rate of more than CAN’s maximum 1 Mbits/sec is needed. For example, a transfer rate of at least 6 Mbits/sec is required for the transmission of digital TV signals and high-quality audio. For this reason, MOST network is defined to handle transfer rate in excess of 20 Mbits/sec. MOST networks make use of the large available raw bandwidth in optical fibers, with optical transceivers connected to each MOST node. Nodes are normally connected in the form of a ring-bus. Finally, wireless networking standards such as Bluetooth (Bluetooth, 2008), Ultra-wideband (UWB, 2008) and related wireless USB (USB, 2008) are expected to play roles in the future in-vehicle data networking solution. Wireless networks have the key advantages of easy installation and zero risk of cable-related damages. However, more research is required to ensure that these networking standards can adapt well in a relatively noise vehicular environment. Another key consideration is the protection of data being transmitted by these means.
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
Figure 5. Multiple modes of remote data communications (1)Request
Application Server
(1)Response
Wireless Channels (4)
(2)Data
(3)Request (3)Response
V III. REMOTE DATE COMMUNICATIONS With the proliferation of wireless communication infrastructure and technologies, plus inter-operator roaming arrangements, sending and receiving data remotely is already a reality (IEEE 802.11p, 2008; DSRC, 2008; IEEE802.11, 2008). To support CAN data communications, many of the standardized higher layer CAN protocols such as CANopen, CAN Kingdom (see Section III) will allow for seamless migration to IP-based cellular data services (e.g. GPRS, HSDPA, EDGE), and thus realize remote CAN data communications easier. However, what is not so certain is the need to ensure timeliness and reliable communications of CAN data between the vehicle and say application servers. As shown in Figure 5 above, there are typically few different modes of communications between the moving vehicles and data server (typically housed within a data centre), as well as among the vehicles themselves. 1. The first ((1) in Figure 5) is server requests and vehicle responses. This mode of communication is achieved by vehicle communicating via a road-side communication infrastructure or cellular phone system. It is typically used by the server to configure, disseminate control information to the vehicles, and
even availability test of the communication channel between the server and the vehicle. 2. Using the same communication channels as the above, the second mode of communications caters for periodic data sent from the vehicles to the server and vice-verse. This mode of communication is also used for the communication of monitored data including non-real time as well as urgent/emergency data from the vehicles to the server and vice verse. 3. In this mode, again using the same communication channels as the first two, the vehicles are able to query server for information pertaining to server’s parameters, as well as link test the return channel availability – this is required as there is often the case that wireless channels are of asymmetric nature. 4. Finally, data can also be exchanged via car-to-car communications (DSRC, 2008); IEEE802.11, 2008). This ad-hoc communication technology is to be implemented in each participating vehicle and enable the autonomous exchange of data between them in a speedily manner without going through the road-side infrastructure and server as in previous three cases.
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Indeed, it should be obvious from the above that depending on the mode of server-vehicle communications, and the nature of CAN data, there would be different transmission requirements (e.g. tolerable delay, loss and bandwidth needed etc.) for each of the communication modes above. Whilst today cellular data services support roaming and base-station handover effectively, with some schemes even allow for seamlessly transfer of access to other wireless broadband internet technologies such as WIFI, EDGE, WIMAX and vice verse. There are other constraints such as cost, coverage and end-to-end delay of these basic communication services that need to be taken into consideration; another word, establishment of basic data connectivity is required but it will not satisfy the quality-of-service (QOS) requirements for remote CAN data communications. An enhanced solution would include additional software functions such as a link QOS manager and a connection manager to be added at the communication infrastructure provider end, as well as additional software components to be included into the monitoring server ends to facilitate link QOS measurements (latency, bandwidth, cost etc) between the data server and vehicles being monitored. Constant monitoring and proactive handover and roaming actions will be initiated by the QOS manager and connection manager to ensure that data communication QOS between the server and vehicles is satisfactory and cost effective. Furthermore, end users should be prompted in the event where coverage of the network is insufficient or the required QOS cannot be met given the available communication channels. Finally, standard for future car-to-car (i.e. adhoc) and car-to-infrastructure (i.e. in the form of dedicated short-range communications (DSRC) and/or IEEE 802.11p) communications are also being developed by the international community. The deployment of DSRC can take many forms, ranging from delay-tolerant “data filling” where DSRC nodes are deployed every few kilometers apart (e.g. on lamp posts) to communicate with
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moving vehicles once they come within the coverage range, to hop-by-hop ad hoc vehicle-to-vehicle communications. Whilst these emerging standards are not expected to substantially change the way vehicular data is being remotely monitored, one can expect improvement in cost as well as communication availability once these standards are fully adopted and implemented (Kosch, T., 2004; Matheus, K. 2002).
IX. DATA SEURITY With the emerging vehicular networks in the forms of infrastructure-to-car (I2C), car-to-car (C2C), car-to-infrastructure (C2I) communications, as well as in-car CAN ECU described so far, a variety of applications for safety, traffic efficiency, driver assistance, as well as infotainment to be incorporated into future automotive designs. At the same time, networked CAN ECUs are increasingly being deployed in cars to realize functions such as engine management, air-bag deployment, and even in intelligent brake systems. However, the availability of these exciting automotive applications can also lead to formidable set of exploits and vulnerable to attacks, as more and more data is being exchanged and communicated in and out of a car. Therefore, one of the most important aspects of remote vehicle monitoring is to ensure the integrity of CAN data being exchanged between the vehicle and monitoring application server. Unlike other communication networks, the vehicular network differs in size, speed of the vehicles, as well as relevance of their geographic position. Most importantly, the sporadic connectivity between them also made providing data security to vehicles especially challenging. There are some possible security threats and attacks (Raya M., Hubaux J.P., 2005; Hubaux, J.P., Capkun, S., & Luo, J. 2004; Zarki M.E., Mehrotra S., Tsudik G., & Venkatasubramanianm N., 2002; Eymann T.,2006) due to the limited security:
Enabling Secure Wireless Real-Time Vehicle Monitoring and Control
1.
2.
3. 4. 5.
6.
Eavesdropping: Eavesdrop and record of a warning message about emergency vehicle, or diffuse wrong information in the network to affect the behavior of other drivers (e.g., divert traffic from a given road and thus free it). Denial of service: Accessibility of a service is restricted (e.g., channel jamming and aggressive injection of dummy messages). Bogus information: Faking of a warning message. Spoofing: Take over of the identity of an authorized device. ID disclosure of other vehicle: Surveillance of the vehicle motions by using the C2C and C2I infrastructure. Cheating with sensory information: Alter the perceived position, speed, direction, etc. in order to escape liability notably in the case of an accident.
What is required is an integrated info-security solution to manage and protect the critical data for intra-car, car-to-car and car-to-infrastructure communications. Respective key information security issues to be addressed can be listed as follows: 1.
2.
3.
Intra-car communications: Anti-theft and confidentiality of real-time sensor and ECU data; Car-to-car communications: Messages exchanged must have stringent constraints on confidentiality, integrity, privacy and time-sensitivity; finally Car-to-infrastructure communications: Integrity of traffic-related information, future entertainment related data (e.g. songs and video), and even liability-related messages must be protected. Other issues include the availability of network infrastructures to provide communications.
The above solutions should ensure that data within and without the automotive is protected from abuse and security attacks. In order to address this holistically, one begins by identifying the various security threats and attacks that various vehicular applications and communication networks are vulnerable to. Next security analysis on these networks and applications needs to be carried out in each of these cases. And finally, proposed solutions and design will include three major aspects: tamper-resistant software in embedded devices for intra-car security, security solutions for vehicle ad-hoc network (VANET) and, last but not least, standard compliant.
X. An OVEALL ARCHITECTURE In summary, Figure 6 shows an overall architecture of the secure wireless real-time vehicle monitoring and control. The various components of this architecture have been elaborated previously in this chapter. The in-vehicle node is responsible for the interfacing and monitoring of vehicle via the in-vehicle networks such as CAN and MOST; it is also capable of conveying control data from the backend applications to the vehicle. Another key function of this component is to communicate securely with the other vehicles via its C2C and/or C2I communication protocol stack. In C2C mode of communication, it often involves the exchange of real-time messages either with one of the vehicle within its range, or broadcast to all the vehicles within its range. The C2I mode on the other hand, is with the stationary service node where communication can be via ad-hoc and/or cellular data service such as GPRS. The main functions of the service node include communicating with the on-the-road vehicles via their in-vehicle nodes, as well as facilitating the request, access and the delivery of one or more car-telematic services and applications to the vehicles. The service node therefore enables the
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provision and delivery of IT applications to the on-the-road vehicles in a secure and transparent manner. As shown in Figure 6, with the help of a service node, services are made available to mobile digital devices such as smart phones and wireless laptops, as more and more of these devices are being carried by drivers and passengers alike. Finally in Figure 6, the various applications and services are being offered via the backend cloud service middleware to ensure that they
are available in a feasible (e.g. transferring from smart phone to in-car display terminals), reliable and secure manner between the backend hosted services and vehicles. The details involved are beyond the scope of this chapter, but key middleware functions here include secure single sign-on for users, user profiles management, accounting and charging, as well as flexible session monitoring and control. It is important to note that a number of vendor-based de-facto standards have started to
Figure 6. Overall architecture of secure wireless real-time vehicle monitoring and control v ideo server
Locationbased server
b ackend v ehicular Telematic s ervices and Applications
Traffic Mgt
s ecurity data portal
s ervice s ignaling data s ervice Node C2I and Cellular s ecure Communications
Mobile devices
C2C In-vehicle Node
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emerge (GEARS, 2008; LiveMesh, 2008), as well as international standard bodies have defined and are currently working on standards in these areas, especially those which are related to networks and communications (WAVE, 2008; C2C, 2008).
XI. FUTURE TRENDS As automotive vehicles continue to play an important role from a country’s economy to individual life style, efforts in making a car better in terms of comfort, safety, reliable and even fun to drive will continue relentlessly. One of the key trends along this development has been the proliferation of embedded data monitoring and control network systems within a vehicle (aka networked vehicle). In this chapter, we elaborated one of the most popular in-car networking technologies called Controller Area Network (CAN). It is also worth noting that newer and emerging in-car network technologies such as FlexRay and Media-Oriented System Transport (MOST) are also being deployed in higher-end vehicles. The ability to monitor and control a vehicle wirelessly for safety, maintenance, reliable and entertainment purposes is fast becoming a reality in not too distance future. Indeed, as more digitized contents find their way to the vehicles in the form of down-loaded and streamed audio and video infotainment-on-the-go applications and services, the availability of higher speed like MOST will become more urgent.
XII. CONCLUS This chapter began by introducing the basic CAN technologies and subsequently focused on elaborating the potential applications and related technical challenges in achieving secure remote monitoring and control of vehicles via CAN. Furthermore, the details of various modes of wireless communications were presented. Last but certainly not least, the importance of protecting
data and information between the vehicle and application server was also discussed. In conclusion, the author sees innovation in real-time monitoring and control area being the key research platform to kick-start a potentially fertile ground for future automotive innovations and services.
RE AALCAR. (2008). Automotive CAN Network Diagnostics. Retrieved October 13, 2008, from http://www.aa1car.com/library/can_systems. htm Audi CAN-bus. (2008). Trainer Information. Edition 1 Bluetooth. (2008). Bluetooth standard. Retrieved October 13, 2008, from http://www.bluetooth. com CiA. (2008). CAN in Automation. Retrieved October 13, 2008, from http://www.can-cia.org/ C2C. (2008). European based Car-to-Car communication Consortium. Retrieved October 13, 2008, from http://www.car-to-car.org DSRC. (2008). Dedicated Short Range Communications. Retrieved October 13, 2008, from http://grouper.ieee.org/groups/scc32/dsrc/ Eymann, T. (2006). The EASIS Security Architecture Approach. 1st C2C-CC Security Workshop. Berlin, November 2006. FlexRay. (2008). FlexRay In-vehicle Network. Retrieved October 13, 2008, from http://www. interfacebus.com GEARS. (2008). Google GEARS. Retrieved October 13, 2008, from http://gears.google.com Hubaux, J. P., Capkun, S., & Luo, J. (2004). The Security and Privacy of Smart Vehicles. IEEE Security and Privacy Magazine 2(3) (2004), 49-55.
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IEEE 802.11p. (2008). Retrieved October 13, 2008, from http://en.wikipedia.org/wiki/IEEE_ 802.11p IEEE802.11. (2008). IEEE 802.11 working group. Retrieved October 13, 2008, from http://grouper. ieee.org/groups/802/11/ Johansson, K. H., Törngren, M., & Nielsen, L. (2005). Vehicle applications of controller area network. In D. Hristu-Varsakelis & W. S. Levine (Ed.), Handbook of Networked and Embedded Control Systems, Birkhäuser. Jaman, G. (2007). Structural Monitoring Using Wireless Sensors and Controller Area Network. IEEE 5th Annual Conference on Communication Networks and Services Research (CNSR’07). Kosch, T. (2004). Technical Concept and Prerequisites of Car-to-Car Communication. Retrieved October 13, 2008, from http://www.car-to-car. org/fileadmin/dokumente/pdf/C2C-CC_presentation_3_BMW.pdf Kvaser. (2008). CAN Kingdom. Retrieved October 13, 2008, from http://www.kvaser.com/index. htm LIN. (2008). Local Interconnection Network (LIN). Retrieved October 13, 2008, from http://www.interfacebus.com/Design_Connector_LIN_Bus.html LiveMesh. (2008). Microsoft Live Mesh. Retrieved October 13, 2008, from http://news.cnet. com/8301-10784_3-9926578-7.html
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Matheus, K. (2002). Car-to-Car Communication – Market Introduction and Success factors. Retrieved October 13, 2008, from http://www.car-tocar.org/fileadmin/dokumente/pdf/C2C-CC_presentation_4_VW_Audi_Carmeq.pdf MOST. (2008). Media Oriented System Transport (MOST). Retrieved October 13, 2008, from http://www.interfacebus.com/MOST_Bus_Interface.html Raya, M., Hubaux, J.P. (2005). The Security of Vehicle Ad Hoc Networks. Proceedings of the ACM Workshop on Security in Ad hoc and Sensor Networks (SASN’05), pp. 11-21, Alexandria, Virginia, USA, November 2005. TTCAN. (2008). Time Triggered Controller Area Network (TTCAN). Retrieved October 13, 2008, from http://www.ttcan.com/ USB. (2008). Wireless USB. Retrieved October 13, 2008, from http://www.usb.org/wusb/ UWB. (2008). Ultra-wideband Technolgy. Retrieved October 13, 2008, from http://www.intel. com/technology/comms/uwb/ WAVE. (2008). IEEE Wireless Access in Vehicular Environment (WAVE), IEEE 1609. Retrieved October 13, 2008, from http://standards.ieee. org/announcements/pr_P1609.0_new.html Zarki, M. E., Mehrotra, S., Tsudik, G., & Venkatasubramanianm, N. (2002). Security Issues in Future Vehicular Network. Proceedings of European Wireless Conference 2002.
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Chapter VI
MAC and Routing Protocols for Vehicle to Vehicle Communications Xiaobo Long Rensselaer Polytechnic Institute, USA Biplab Sikdar Rensselaer Polytechnic Institute, USA
ABSTRACT Numerous efforts are currently under progress to enhance the safety and efficiency of vehicular traffic through intelligent transportation systems. In addition, the growing demand for access to data and information from human users on the go has created the need for advanced vehicle-to-vehicle and vehicleto-roadside communication systems capable of high data rates and amenable to high degrees of node mobility. Vehicular communications and networks are expected to be used for a number of purposes such as for enabling mobile users to transfer data and information from other networks such as the Internet and also for implementing services such as Intersection Decision Systems (IDS), Automated Highway Systems (AHS), and Advanced Vehicle Safety Systems (AVS). In this chapter the authors describe medium access control (MAC) and routing protocols for vehicular networks and the various factors that affect their design and performance.
Introduction and Background PHY Layer Issues Vehicle-to-Vehicle (V2V) communication systems, as part of future Intelligent Transportation Systems (ITS), require bidirectional communica-
tion between road-side beacons and individual vehicles. A recent standard for vehicle-to-vehicle communication is the 5.9 GHz Unlicensed National Information Infrastructure (UNII) been developed to support high mobility in IEEE 802.11 protocol. This emerging 802.11 standard for 5.9 GHz is generally referred to as Wireless Access in the Vehicular Environment (WAVE),
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MAC and Routing Protocols for Vehicle to Vehicle Communications
which provides Dedicated Short Range Communication (DSRC) for future vehicle-to-vehicle communications. The radio link of these communication systems has a range varying between a few meters and hundred meters (Eichler, 2007). Wireless LAN infrastructure is employed to build inter-vehicle communication based applications. WAVE networks consist of seven 10MHz wide channels, with Channel 178 being a control channel and the other 6 being service channels. The channel switching mechanism in IEEE 802.11h is adapted to perform the multi-channel operation. The physical (PHY) and Medium Access Control (MAC) layers of 5.9 GHz DSRC are based on IEEE 802.11a PHY, which uses an OFDM (Orthogonal Frequency Division Multiplexing) waveform and the IEEE 802.11 MAC respectively. However, the vehicular environment and the requirements for fast access introduce unique requirement on the MAC functionality. While IEEE 802.11 is intended for stationary or low mobility environment, communication in mobile WLAN (wireless local area network) must be able to transfer messages to and from vehicles at speed up to 120 miles per hour. Vehicular traffic scenarios pose greater challenges than the indoor WLAN applications, due to associated driving speed, varying vehicular traffic patterns and driving environments. The new standard is expected to establish requirements for packet error rate performance in these challenging environments. The IEEE is currently working on the IEEE 802.11p Wireless Access in Vehicular Environments (WAVE) standard. There are two types of inter-vehicle communication in WAVE. One is inter-vehicle communication using AP (Access Point). Access points may be provided or co-located at street corners, traffic lights, emergency phones, parking lots, rest areas, gas stations or other shops in service areas. This approach requires development of exclusive infrastructure and is cost expensive. The other is inter-vehicle communication supported by wireless ad-hoc network. In this mode, there is no distributed 802.11 beaconing mechanism. Only
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the basic carrier sense mechanisms are used, and the RTS (request to send)/CTS (clear to send) access is prohibited, with the purpose to facilitate fast access to vehicles. In ad-hoc network, each mobile vehicle detects other’s position and routing path. The other potential candidate of vehicle-to-vehicle communication protocol is the IEEE 802.16e Wireless Metropolitan Area Network (WMAN) standard, operating in the 2-6 GHz frequency band. In Europe, a microwave frequency of 5.8 GHz is recommended. Two or four channels with 5MHz bandwidth each and a maximum Equivalent Isotropically Radiated Power (EIRP) of 3 db Watt are available (Baranowski, Lienard & Degauque, 1991). In vehicle-to-vehicle communication, multipath fading is a major problem for short range mobile microwave links. Multipath fading is caused by reflected waves from the road, vehicles, traffic signs and buildings, which interfere with the direct signal. As a result, the received signal strength is position dependent and time varying. The interference of reflected waves can be destructive to the received radio power and lead to signal outages. Usually the reflected waves have less amplitude than direct wave in vehicle communications and the wave components can be absorbed, scattered or reflected. The received power is determined by the radiating characteristics of beacon and the vehicle antenna as well as statistically distributed reflectors. To study the link performance in multipath fading channels, both deterministic channel models and statistical channel models are developed in literature. A deterministic channel model was derived in (Pfeiffer & Schrei, 1994) for short range radio link, Pr = PG t r Gt (
λ0 1 ) | T |2 4π nt
(1.1)
where the subscript t and r denote the transmitter and the receiver antenna, respectively. P indicates
MAC and Routing Protocols for Vehicle to Vehicle Communications
the signal power, G is the antenna gain and λ0 is the wavelength in free space. nt is the number of transmitter antenna elements. Parameter T is determined by many factors, including the number of transmitter and receiver antenna elements, the number of propagation paths and their length, the radio radiating patterns and the reflection factors of the reflecting surface. In statistical models, the propagation channel can be modeled as a time varying linear filter, the impulse response given by (Wang, Sen & Matolak, 2007), L −1
L −1
0
0
h(τ,t) = ƒ zl (t )α l (t )e jθl ( t ) = ƒ hl (t )δ(τ − τl (t ))
(1.2)
Where αl(t) and θl(t) represents the amplitude and phase of the lth received wave component, which has a time-varying time delay τl(t). The δ function is the Dirac delta. Compared to conventional wireless channels, vehicle-to-vehicle environments can have frequent and rapid channel changes due to mobility and low transmitting and receiving antenna heights. Thus multipath components are modeled as on/off process in (Wang, Sen & Matolak, 2007). z(t) in Equation (1.2) is the persistence process which is used to account for the finite lifetime of propagation paths. The on/off process z(t) is modeled by a Markov chain. The received signal strength for the empirical channel is modeled using Weibull distribution given by (Wang, Sen, & Matolak, 2007) for channels in 802.16e systems where distance between transmitter and receiver is large, Pw ( x) = β / αβ xβ−1 exp(−( x / α)β )
(1.3)
Where β is a shape factor that determines fading severity, α = E ( x 2 ) / Γ(2 / β + 1) is a scale parameter, and Γ is the gamma function. β = 2 yields well known Rayleigh distribution, and β i) approaching each other. The corresponding probabilities are described below: Vehicle i is an aggressive driver and ve• hicle j is a defensive driver, pij = αβ; • Vehicle i is a defensive driver and vehicle j is a slow driver, pij = βγ; • Vehicle i is an aggresive driver and vehicle j is a slow driver, pij = αγ. The relative velocity of any node i and j approaching each other, δij is assumed to be represented by the relative speed between adjacent lanes with a white noise:
5.
(2)
To facilitate the further discussion we need the following notations: • • •
•
•
xtij: The distance between vehicle i and vehicle j at time t. p t ij: The probability of the event that xtij i + 1, the distance between nonadjacent vehicles also obeys normal distribution with mean |j – i|Hs and variance |j – i|2σ2s. Let p(xij δ’ in order to find the slot for the ramp car. The second strategy is a velocity-based algorithm
Table 3. Traffic merging algorithms overview Knowledge
Right of Way
Assumption
Distancebased
Position
The car closest to the merging point
Velocity does not vary much
Velocitybased
Position Velocity
The car first arrives at the merging point
Acceleration does not vary much
(referred to as V) that also takes the velocity into account. It calculates the time for a car to arrive at the merging point and compares the time for cars from the two traffic streams. The car with shortest time merges first. The third strategy is platoon-velocity-based merging algorithm (referred to as PV). It is a more refined version of the velocity-based merging algorithm V. Instead of comparing the ramp car against one main road car as in V, PV compares the ramp car with a group of main road cars on the segment SO and assigns a slot between two main road cars for the ramp car. The slot is chosen based on the arrival time calculated at S. In Table 3, we summarize distance-based and velocity-based merging algorithms.
Figure 2. Notations of proactive merging algorithms
Figure 3. Illustration of lane switch in CPV
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Proactive Traffic Merging Strategies for Sensor-Enabled Cars
Each algorithm performs better than others under certain traffic condition. A refined adaptive algorithm switches to the strategy that is best fit for the current traffic situation. More details of single lane merging algorithms can be found in (Wang et al., 2007).
Multiple Lane Scenario For main roads with multiple lanes cars are able to change lanes to overtake other cars or to make space for new arriving ramp cars. Our experiments will show that PV is the most effective proactive merging algorithm for main road with a single lane. Thus, we extend it to roads with multiple lanes. In addition, we develop a refined version of PV, which we call Cascading PV (referred to as CPV). CPV assumes two lanes but can be generalized to roads with more lanes. Cars on the outer lane and the inner lane of the main road can change lanes prior to the merging area to create a larger gap for the merging ramp cars as shown in Figure 3. CPV aims to maximize the minimum safety distance on both lanes which might result in a lane switch between two cars. Multi-lane merging is an open research area with contradictory reports. The simulation results we show in the Discussion section demonstrate that appropriate lane changing on the main traffic stream before the merging area calculated by CPV results in better performance than PV. Hidas (2002; 2005) proposes a lane changing model for merging scenarios. This model distinguishes forced and cooperative lane change from free lane change, which helps modelling the merging behaviours under congested traffic conditions. Sarvi and Kuwahara (2007) model the ramp car behaviour and the main road back car behaviour. They report that lane changing on the main road before and/or after merging area can cause capacity reduction. There may be a number of reasons why lane changing can lead to different results for
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different merging algorithms. These approaches have different underlying microscopic traffic models and use different criteria to determine whether or not a car should change its lane. In addition, all approaches use different simulations. Most studies (Hidas, 2002; Hidas, 2005; Sarvi & Kuwahara, 2007) simulate an open boundary road section, whereas in our simulation the main road is designed to be a loop. The loop provides a feedback to show the impact of merging algorithms. In summary, future research is required to explain the different findings.
EVAaluation This section shows how to assess merging algorithms by identifying a range of performance criteria, such as delay, flow, and fuel efficiency. It will also discuss how and why sensor-enabled cars can improve merging algorithms.
Performance Metrics Merging algorithms can be evaluated from two perspectives: safety and efficiency. Although all merging algorithms have to ensure road safety our major research aim is to optimize the traffic throughput. In addition, we also consider other factors than efficiency such as fuel consumption. In this section, we introduce criteria for traffic control algorithms and show how to measure the performance. We identify which criteria depend on each other and illustrate how optimized throughput implies higher speeds of cars coupled with tighter gaps, while safety requirements imply lower speed and larger gaps.
Safety To evaluate the safety characteristics of a merging strategy we consider two types of events.
Proactive Traffic Merging Strategies for Sensor-Enabled Cars
•
•
Collision: There is more than one car at the same position of the road at the same time. Near-collision: The gap between two successive cars is smaller than the minimum safety gap but if the front car does not brake a collision does not occur.
•
We distinguish those two events and count the number of collisions and the number of nearcollisions relative to the total number of cars in a time frame.
Efficiency A good merging strategy utilizes the road resources more efficiently. We use the following criteria to evaluate efficiency. •
•
Flow: The average number of cars passing a certain position over a period of time. We evaluate the flow as the product of density and velocity over the entire main road. The largest flow would occur if cars are bumper to bumper (maximum density) and move at the speed limit (maximum velocity). Clearly, this is not achievable in reality for safety reasons. If the density is maximal then cars are not moving, yielding a minimum traffic flow of zero. Therefore, the maximum of traffic flow occurs at a certain density with a corresponding velocity. Capacity: The maximum number of cars passing a certain position over a period of time, i.e., the maximum traffic flow. Traffic congestion occurs when the demand exceeds the capacity, thus achieving higher capacity can effectively avoid traffic congestion. However, the real capacity is typically lower than the theoretical maximum capacity because it is not only determined by the road configuration (e.g., the number of lanes, the lane width) but also by disturbances in the traffic flow, such as lane changing or merg-
•
•
ing. We expect that a merging strategy has a significant impact on the real capacity of a road. Therefore, the observed reduction of the real capacity from the theoretical capacity is an important criterion for merging strategies. Stability: The ability to adapt to a new traffic situation. It impacts the capacity and the total number of merged cars. We expect that a stable merging strategy achieves higher capacity by accommodating more merged cars. A merging car impacts the stability of two traffic streams because in one traffic stream a new car is added and in the other stream this car is removed. As a result, the cars of both streams adjust their speeds and gaps to achieve approximate equilibrium conditions. As the density of one stream increases the gaps between the cars of this stream decrease until no farther cars can merge without violating the safety distance. The gaps between the cars distribute more evenly in the stable traffic flow than the dynamic traffic flow. Thus, some larger gaps exist in the dynamically changing traffic flow and can accommodate more cars to merge. Delay: The total required time for a strategy to fill the target lane with a desired number of merging cars. It describes how quickly the system absorbs merging cars. In both merging scenarios, the car (on the ramp or on the blocked lane) locates a gap from the adjacent lane and adjusts its speed to fit in the gap in order to merge. A bad merging strategy may cause more delay for the merging stream because the car has to wait if it cannot adjust its speed appropriately to merge. Fuel consumption: Varies as much as 45% (Victoria, n.d.) between different drivers using identical cars. Multiple studies show that accelerating slower will dramatically improve fuel efficiency: the author in (Reed,
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Proactive Traffic Merging Strategies for Sensor-Enabled Cars
2005) found fuel savings up to 37% with an average of 31% simply by accelerating a car slowly. Similar findings (Parker & Stedman, 1994) show slower acceleration saving between 14% and 21% of fuel use over aggressive acceleration. Different merging strategies have a significant impact on the acceleration.
Simulation We developed a Java-based controlled simulation environment to test various traffic merging strategies. It is based on an existing microscopic traffic model IDM (Treiber et al., 2000), which we presented in the Longitudinal Models section. IDM specifies the following parameters: maximum velocity, safe time headway, maximum acceleration, comfortable deceleration, and minimum distance. Certain values guarantee collision-free driving. We compare the performance of proactive merging algorithms against a priority-based
algorithm in a variety of simulation settings. Our work shows the following three parameters are most important to measure the efficiency of a merging algorithm: the ramp length, the initial density of the main road, and the rate of incoming ramp cars. In the simulations, we initially study uniformly distributed traffic flow and then we study arrival ramp traffic following Poisson distribution.
Discussion In the proactive merging strategies, the actual merging point is dissociated from the decision point where to merge. This decoupling results in a significant reduction of traffic merging delay at the expense of a lower overall average velocity on the main road. A lower velocity is expected because higher traffic load on a road requires a smaller minimum safety distance, which is only achievable by a lower velocity. Figure 4 shows the performance of different merging algorithms. The
Figure 4. The performance of merging algorithms with a short ramp of 100 m and high ramp flow rate
PV
CPV
Total Number of Cars
R
Delay (min)
30 20 10 0 110
150
190
230
270
R
400
200 100 1
310
5
10
CPV
R
30
Max
velocity (m/s)
Flow (cars/min)
20
25
(b)
65 55 45 35 25
PV
CPV
25
20
15 1
5
10
15 Time (min)
(c)
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15 Time (min)
(a) PV
CPV
300
Total Number of Cars
R
PV
20
25
1
5
10
15 Time (min)
(d)
20
25
Proactive Traffic Merging Strategies for Sensor-Enabled Cars
dashed line in Figure 4.c illustrates the maximal flow in equilibrium condition. R refers to the priority-based merging algorithm. The strategy R performs less well because once a high load is achieved R simply breaks, i.e., no farther cars can merge. CPV is a well performing strategy. It approaches the theoretical maximum flow. As expected, CPV has a smaller overall average velocity because it is the only way to achieve a higher traffic load. In general, the advantages of proactive merging algorithms over the priority-based algorithm are the following: •
• •
The delay in proactive merging algorithms decreases by a third compared to the prioritybased algorithm before the loop is saturated. Cars on the ramp can merge into the main road more quickly. The capacity of the main road increases by up to 67%. The traffic flow in proactive merging algorithms is higher. Since the traffic flow is defined by the product of the average velocity and the density, there are more cars on the main road in proactive merging algorithms than the priority-based algorithm due to the smaller delay. At first, the larger number of cars overcomes the slightly smaller average velocity; then as more cars merge in, the main road starts getting saturated, the average velocity drops to ensure the safety distance.
A disadvantage is the slightly lower average velocity in proactive merging algorithms. This is a side-effect of a larger number of cars merging into the main road. The distance between cars gets smaller, which leads to a lower velocity because the velocity depends on the minimum safety distance between cars. However, this lower overall velocity is simply a byproduct of the higher traffic flow. We must manage complex tradeoffs among several factors, in particular the delay, flow, and
velocity. The traffic flow increases at the beginning as the ramp cars merge, peaks before the main road is saturated, and then decreases as the average velocity decreases. In order to maintain a high traffic flow as well as to accommodate a large number of ramp cars, we need to find a balance between the velocity and the number of cars that merge into the main road. Overall, CPV leads to remarkable results in terms of delay and flow. It assigns a slot for a ramp car among a group of main road cars in the segment between the decision point and the merging point, and creates a larger gap for the ramp car by switching lanes of the cars on the main road prior to the merging area. In summary, CPV optimizes the delay and flow at the expense of the average velocity on the main road. On the other hand, the lower average velocity enables CPV to achieve a significant increase for the total number of cars on the main road.
Futurrnd There are two research directions that we believe will draw future attention: •
Robustness. Proactive merging strategies rely on two assumptions. First, the spatial information monitored by sensor-enabled cars is accurate and precise. Although automotive sensors are typically required to have a total error less than 3% over their entire measured range (Fleming, 2001), Hernandez (2005) also reports that for car speed lower than 5 km/h sensors give skewed and misleading information. Consequently, future research will need to focus on the impact of imperfect information. Second, we envision that navigation systems not only provide route guidance but also provide recommendations of how to drive cooperatively with the vehicles in the vicinity. Therefore, a merging algorithm must accommodate various
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Proactive Traffic Merging Strategies for Sensor-Enabled Cars
•
human factors, such as reaction times, and be prepared to handle drivers who do not follow the system’s recommendations. Scalability. Currently, proactive merging algorithms are localized and provide only recommendations for a single intersection. Highly scalable algorithms are required to optimize traffic flows at a network level that involves a large number of intersections as well as multiple on-ramps and exit-ramps at freeway networks. Scalability can be achieved through locality in both time and space. Sensor-enabled cars will detect information in their spatial vicinity only use it for decisions in the near future as long-term estimations occur high computational complexity. As a result, we expect that control strategies will be distributed consisting of local decisions according to the neighborhood conditions, instead of having a single centralized system managing the entire traffic network. We expect that such a strategy will scale well. Novel models with a sufficiently fine level of granularity will attract further research.
ConcluSION This chapter provides an overview of new ideas and approaches in the area of traffic flow control with the assistance of VANETs. We report on the state-of-the-art in this area in terms of vehicular traffic theories, traffic models, merging strategies, and performance measurement methodologies. In this chapter, we have highlighted proactive merging strategies and the potential benefits of applying sensor-enabled cars. We have shown in our research the significant improvement in traffic flow and decrease in travel time mainly result from the decoupling of the merging point and the decision point, and multilane optimizations,
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such as pre-lane-changing. We have identified potential research directions in the robustness and scalability of merging algorithms. Assisted by advanced sensing and communication technologies, traffic control strategies and merging algorithms will lead to more efficient use of the current road networks and ultimately help to alleviate traffic congestions.
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Chapter XI
The Localisation Problem in Cooperative Vehicle Applications Javier Ibañez-Guzmán RENAULT S.A.S., France
ABSTRACT The next paradigm towards enhancing vehicle safety and road transportation represent cooperative systems. Advances in computer and communications technologies are facilitating the establishment of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications links. This enables the sharing and aggregation of information which results in an extension of the driver awareness horizon in an electronic manner. In this chapter, V2V and V2I applications are considered as a spatio-temporal problem. The tenet is that sharing information can be made only if this is time stamped and related to a spatial description of the information sources. The chapter formulates the spatio-temporal problem having as constraint the precision of the pose estimates of the vehicles involved. It regards the localisation problem and accuracy of digital road maps as a combined issue that needs to be addressed for the successful deployment of cooperative vehicle applications. Two case studies, intersection safely and an overtaking manoeuvre are included. Recommendations on the precision limits of the vehicle pose estimations and the potential uncertainties that need to be considered when designing V2V and V2I applications complete the chapter.
Introduction Over the years motor vehicles have provided a rapid means of transportation, they are today part of our daily lives. These have evolved from
simple thermo mechanical devices to complex mechatronics systems where networks of microcomputers plus a plethora of proprioceptive and exteroceptive sensors control multiple functions. The dependency on motor transport has resulted in
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The Localisation Problem in Cooperative Vehicle Applications
traffic congestions, pollution and an unacceptable level of accidents (Broughton & Walter, 2007). Whilst in countries with advanced economies, the number of accidents has decreased, in countries with emerging economies accident tendencies are on the increase, in most cases with disastrous consequences. Since the late seventies, the number of safety features in all types of vehicles has increased. These are known as passive safety systems like seat belts, deformable chassis, etc. At the same time legislation has hardened and road infrastructure has improved. Currently the number of accidents and road fatalities has decreased particularly in Europe, Japan and the USA; however over the past years a plateau is being reached and further reductions are difficult. At the same time congestion and pollution continues to increase whilst society is becoming more concerned on the costs of pollution to global warming and health under an uncontrollable raise in fuel costs. In the past years, the trend has been to develop active safety systems as part of advanced driving assistance systems (ADAS) like the use of video cameras for obstacle detection, Radar or Ladars (Laser detection and ranging sensors) for vehicle following, etc. In other words, through the use of exteroceptive and proprioceptive sensors, it is feasible to estimate the vehicle ego-state and perceive its immediate surrounding environment. The combination of the captured data results in digital models that represent the vehicle immediate surrounding environment and is enabling the development of advanced safety systems as it is possible to inform and warn drivers of potential risks or even act on the subject vehicle in the case of pre-crash braking systems (Laugier C., Vasquez D., Yguel M., Fraichard Th. & Aycard O., 2008, Wang H., Xu Jian, Ibañez-Guzmán, J, Jarvis, R., Goh, T. & Chang C.W., 2000),. Whilst this represents a major progress and should yield to better safety, there are two major constraints: Sensors: these have limited capabilities and only a combination of several of them could warrant
limited safety; Cost: prices are high and only a limited number of vehicles would be equipped with full safety features. Further, vehicle onboard sensors due to their physics and the limits in their layout on the vehicles, means that the perception horizons are limited which reduces the effectiveness of several active safety applications. Advances in computer and communications technologies are facilitating the establishment of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) wireless communications links (Cook J.A., Kolmanovsky I.V., McNamara D., & Prasad K.V., 2007, Srini V.P., 2006) . This enables the sharing and aggregation of information and hence to extend the driver awareness horizon. Within this context, it is feasible to share information between vehicles and infrastructure, to combine this information and allow for a better machine and driver understanding of its situation and thus to extend electronically the driving horizon (Cook J.A., Kolmanovsky I.V., McNamara D., & Prasad K.V., 2007, Srini V.P., 2006) . A typical example could be cooperative adaptive cruise control (CACC), an extension of ACC, where in addition to measuring the distance to the leader, a vehicle can exchange information with the leader by wireless communications to improve performance (Arem B.van., Driel C. van., and Visser R., 2006). At present multiple worldwide efforts exist in this domain like the SKY project on intersection safety by NISSAN Corp., which is already in its field operational test phase involving hundreds of passenger cars. In Europe there are two major large scale demonstrators’ projects under the 6th research framework of the European Community, namely the co-operative vehicle infrastructure systems (CVIS) and the Cooperative vehicles and road infrastructure for road safety (SAFESPOT) projects, (CVIS Project Partners, 2008 & SAFESPOT Project Partners, 2008). V2V and V2I applications result from the sharing of information between the various entities involved. The purpose is to extend the driver awareness horizon by associating the information
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generated in one vehicle, transmitting it to the relevant vehicles within its immediate environment so as to share information. The association is based in two important parameters, time and space. In this chapter, V2V and V2I applications are considered as a spatio-temporal problem. The tenet is that the sharing of information can be made only if this is time stamped and related to a spatial description of the information sources. That is, all applications need to have a spatial description with respect to something and for this to be usable they need to be referred not only with respect to an absolute reference frame but also to a spatial description of the environment like for example the position on a road, the position of the vehicles with respect to an intersection and so on. The scope of this chapter does not include the study of techniques used to enhance the vehicles pose (position and attitude) estimates as these are addressed in much detail by work elsewhere (Schlingelhof M., Betaille D., Bonnifait P. & Demaseure K., 2008, Lee K.W., Wijesoma S. & Ibanez-Guzman J., 2007 & Cui Y.J. & Ge S.S. 2002). This chapter is organised as follows, Section 2 states that cooperative vehicle applications are governed by the solution of a spatio-temporal problem where the main constraint is the precision of the pose estimates of the vehicles involved in the application. Section 3 formulates the localisation problem and accuracy of digital road maps as a combined issue that needs to be addressed for the successful deployment of cooperative vehicle applications. Section 4 completes the problem formulation by studying in a simple manner the case of the use of V2V or V2I communications to traverse safely a road intersection and an overtaking manoeuvre. Finally, Section 5 presents a summary of the chapter including recommendations and comments on the implications on the precision limits of the vehicle pose estimations and the potential uncertainties that need to be considered when designing V2V and V2I applications.
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CoopERratihicl application a SSpatio-TEMmporal pro All types of vehicles travel on networks of roads provided by the infrastructure, and the control of this network is through road and traffic signs to various levels of complexity. Within these networks, sets of vehicles travel guided independently but whose response is constrained by the state of the road network. Communications between the vehicles and infrastructure provides a different network that allows for the dynamic sharing of information amongst all the entities in the road network. Independent of the wireless communications technologies and protocols deployed, two types of links can be established: Between vehicles, that is Vehicle to Vehicle (V2V) communications, and between vehicles and the infrastructure or road side units (RSU), also known as Vehicle to Infrastructure (V2I). The main feature is that the information acquired by the vehicles or infrastructure via their own sensors is shared with other vehicles or with another part of the infrastructure via wireless communications links. This inherent property of Intelligent Transportation Systems (ITS) provides opportunities for developing new applications. In order to define the need for a Spatio-Temporal formulation and to focus on the localisation problem, in this section a succinct list of applications is introduced. For this purpose, applications have been classified into four families: Safety, Mobility, Efficiency and Telematics. Details of each family are given along the following structure: definition, case study, illustrative examples, technological challenges, and Localisation Information. The classification follows the one defined by the Research and Innovative Technology Administration (RITA), U.S. Department of Transportation (Department of Transport, 2008). The emphasis is according to the level of position accuracy required by the applications, with those related to Safety addressed first.
The Localisation Problem in Cooperative Vehicle Applications
Safety A fundamental function for a driver is to be aware of the relationships between its vehicle (subject vehicle-SV), the other vehicles (principal other vehicles-POV), and other entities within its surrounding environment. Today, this awareness is assisted through the use of a series of onboard perception systems that detect obstacles, track vehicles, assist in the vehicle control process, etc. These are known as driving assistance systems whose details are found in the literature (Gharavi H., Prasad K. V. & Ioannou P.A. (Eds.), 2007). These systems are designed to reduce risk during travel by providing means to avoid collisions/ crashes. For this purpose, a series of sensors are embarked onto the vehicles to different levels of complexity. Experience has shown that there is no ideal sensor, as all types of sensors have limitations and hence there is the need to use multiple sources of information by combining their outputs. For this purpose, sensor fusion methods are used to build models of the environment that are then used for decision making and hence informing or warning the driver, or even acting on the vehicle. As demonstrated by the landmark event on vehicle navigation, the DARPA Urban Grand Challenge, today it is possible to generate enriched information with a good degree of certainty so as to guide autonomously ground vehicles in urban like environments (Defense Advanced Research Projects Agency, 2008). These technologies can be transferred to passenger vehicles and are today part of the technological roadmaps of vehicle OEMs. A major constraint is that for this purpose, a combination of sensors is needed operating at fast sampling rates and having a high resolution. Further, due to the geometrical characteristics of passenger vehicles, the layout of these sensors are such that they will always have a limited field of view (Kelly, A. & Stentz, A., 1998), limiting their capabilities.
In order to illustrate this problem, the following use case is defined: Intersection Safety.
Use Case Generic Intersection Safety Figure 1. presents a schematic view of a typical intersection. For description purposes and to ensure the consistency of the descriptions, the vehicles have been classified as follows: Subject vehicle (SV) is the centre of interest and with respect to which the application or function is derived. Intruder Vehicle (IV) represents the vehicle that is to cross the path of the SV representing a direct risk and thus the likelihood of a collision. The Distracter Vehicle (DV) represents vehicle not directly involved in the use case, but rather it acts as an occlusion or is within the neighbourhood of the SV. There are other special vehicles as listed in Figure 1, which are used to complete the scenarios. For the case of the generic intersection safety use case, the subject vehicle (SV) arrives to it thus the driver needs to be aware of the presence of all the vehicles or vulnerable road users that represent a risk. It is assumed that the SV is equipped with front looking perception sensors (e.g. Ladars, Vision Cameras) that are able to detect objects obstructing its path. Making an abstraction of reliability and costs of these systems, it can be observed that the physics of the process limit the driver awareness horizon. Although additional sensors could be included occlusion, their limited field of view and layout on the vehicle are major concerns (Gharavi H., Prasad K. V. & Ioannou P.A. (Eds.), 2007). If speeds increase or the environment is more cluttered, the problem is compounded. Observing the scenario in Figure 1 from the driver or sensor point of view, it is clear that the presence of the Distracter Vehicle (DV) and that of the Two-Wheel vehicle is ignored; both are occluded and thus constitute a risk. Further, supposing that there is only the Intrusion Vehicle (IV) and that this arrives at a prohibited speed, it
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Figure 1. Schematic of a typical intersection in an urban setting
Figure 2. Schematic view of the SV environment using V2V communications
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is impossible for the driver of the SV to know that the IV will not have sufficient space to brake at the stop line due to the speed that it is travelling, presenting a risk to the SV. Multiple situations exist like for example if the High Tonnage Vehicle (HTV) or truck is at a stop, the SV or the Power Two-Wheel vehicle (PTW) are at a risk as they are not aware of such situation. If each of the vehicles can transmit its position, speed and other status data, by associating this information onto digital representation of the context (intersection), an extended awareness can be created, as shown in Figure 2. An application running in the SV then can analyse and identify the possible risks informing consequently the driver beyond what current sensors could provide. For example the DV and the two-wheeler are highlighted in red as potential risks as shown in Figure 2. The vehicle at the rear of the SV could also be warned that it is driving too close, and warning information is sent via a wireless link. From this simplistic diagram, it can be observed that by knowing the spatio-temporal relationships between all the actors, a full awareness model can be created beyond the capabilities of current sensor technologies and ideally avoiding their need altogether, which results in the interest in V2V and V2I technologies.
Applications There are a plethora of applications related to vehicle safety, which involve knowing the location of the entities of interest at a given time and their relative situations (e.g. speeds, inter-distances, etc.). In general, these are known relative to the environment or context which provides information that leads to the establishment of relationships between all objects, that is the complete perceived model of the area of interest (Madhavan R., Messina E.R. & Albus J.S., 2007 & Papp Z., Brown C.L., & Bartels C. 2008). A good panorama of safety related applications can be found at the SAFESPOT site (SAFESPOT
Project Partners, 2008). Sample applications include: Road Hazard Warning. The SV has detected a hazard along its trajectory; this could be an accident, a broken vehicle, road works, etc. This information is geolocalised and broadcasted through the VANET (Vehicular Ad-Hoc Network) to the immediate vehicles. Lane Change Assistance. Changing lanes could create risk situations, due to the presence of vehicles in the driver blind zone. By knowing what occurs within the vicinity of the SV, such as the distribution of vehicles on the appropriate lanes, their relative speeds and inferring the intentions of the driver, the system could generate an advice to the driver on whether or not to concretise the manoeuvre. The use of a blind spot detector will be unnecessary. Overtaking Assistance. The overtaking manoeuvre involves a degree of risk. Drivers need to have a clear line of sight of what is in front or behind the vehicle, and the relative speeds of all the observed vehicles, with respect to their own speed as well as the shape of the road. When drivers’ cognitive and perception capabilities are reduced as they become older, or when visibility is poor, overtaking becomes potentially more dangerous. By knowing the position/direction/speed of the various intervening vehicles, with relation to the SV plus context (road), it will be possible to infer whether there is a high risk involved and consequently warn the driver.
Technical Challenges For safety applications, it is necessary to know precisely and on time the location of the SV and its relationships with other road entities within its immediate manoeuvring area. There is little room for uncertainty or malfunctioning of the system. That is no detection/location mistakes are acceptable and such systems have to prove their field reliability. Knowing the vehicle ego-state is something achievable, however, determining the
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vehicle position and projecting it onto a digital road representation is much more difficult (Schlingelhof M., Betaille D., Bonnifait P. & Demaseure K., 2008, Lee K.W., Wijesoma S. & Ibanez-Guzman J., 2007 & Cui Y.J. & Ge S.S. 2002). If data from on board exteroceptive sensors is to be shared, this poses different challenges in particular on the integrity of this data, in terms of the degree of confidence on the detection effected, and its location with respect to the sensing vehicle. Other issues to take into account are for example information latency, capacity of the communication channels and sampling rates.
Mobility Improving mobility by reducing delay and travel times is a major objective of many ITS (Intelligent Transportation Systems) components (Department of Transport, 2008). Increases in traffic density over the years have become problematic; e.g. costly, increasing pollution, etc. By being able to collect information from vehicles travelling over a wide area, from networks of sensors distributed strategically and from public service organisations/road operators/public authorities, it is possible to centralise this data to fuse it and infer relevant information that leads to the regulation of traffic, the broadcasting of location dependent dynamic information and to address traffic disturbances like accidents. In this case there is observability of the traffic conditions in an instantaneous manner and thus controllability of the road network could be attained. Each communicating vehicle could be considered as a sensor, and the aggregation of the information results in a distributed centralised data fusion problem where statistics will be generated, and control actions will be broadcasted to vehicles and infrastructure. The sharing of information from the infrastructure to all vehicles on an area through the VANET to extend the driving awareness of the drivers is an important characteristic.
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Use Case: Geolocalised Broadcasting of Information Over a road segment it is necessary to control virtually the flow of traffic. For example the infrastructure could broadcast an event over an area, such as the need for a free corridor to facilitate the crossing of an Emergency Service Vehicle (ESV). For this purpose, the information is broadcasted from the infrastructure or through multi-hoping between the relevant vehicles, as shown in Figure 3. The relevant vehicles will then form a blue corridor for the save crossing of the ESV. The schematic in Figure 3 shows the manner in which the information broadcasted can be directed to a target area, so that the information can be used to control the flow of vehicles within that area. In this case, drivers within the neighbourhood receive information on the arrival of an ESV associated to a lane, and accordingly the implicated drivers will leave the lane open for the traversal of the ESV.
Applications Controlling vehicle mobility in a geolocalised manner is a group of applications that can be implemented using ITS technologies. These associate information on other vehicles or events to the road network in a precise manner. For example in Figure 3, if there was a parallel road next to the one where the ESV is travelling and thus the information might be associated to the wrong road, the result could be catastrophic, hence there is a need for precise location information. Most of the applications addressed in projects like CVIS include this type of applications (CVIS Project Partners, 2008). Sample applications include: Navigation and Traffic alerts. Information broadcasts extend the situation awareness horizon of drivers that should allow for vehicle speed optimisation, and best trajectory recommendations based on up to date traffic information. To
The Localisation Problem in Cooperative Vehicle Applications
Figure 3. Traffic control broadcast information Visualisation within the vehicles
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improve traffic flow, the usage of lanes will be organised and speed recommendations will also be issued, etc. Presence of a Slow Moving Vehicle. Drivers will be notified of the presence of a slow moving vehicle on a given lane. Upon detection of such vehicle the information is broadcasted including geolocalisation data. This will be transmitted to vehicles in the neighborhood. These vehicles will then re-broadcast the information to other vehicles, and those heading in the same direction as the slow vehicle will warn drivers of this issue. Monitoring of traffic conditions. In a way all vehicles could be regarded as a collection of wireless sensors that share their information locally and globally. Locally detailed models of the environment could be built using distributed decentralized fusion techniques whilst globally all data is collected by a server. Relevant information will be generated using centralized data fusion and data mining techniques. This will be broadcasted to the relevant areas with its integrity ensured via statistical techniques.
Efficiency The sharing of information over wide areas allows for the optimisation of resources, with the transmission of relevant information on time in a geolocalised manner. This results in an intelligent infrastructure leading to intelligent environments where it will be possible to deploy a collection of vehicles with limited sensing and intelligent capabilities, that is, the efficiency of existing facilities is optimised so that mobility and commerce needs can be met while reducing the needs to construct or expand new facilities (Department of Transport, 2008). The capacity of the road network is increased whilst at the same time by optimising the travelling speeds of an ensemble of vehicles so that it is possible to optimise their own efficiency i.e. fuel consumption. Applications that lie within the efficiency domain augment the throughput of the road network that is the number of persons, goods, or vehicles traversing under safety constraints.
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Use Case Traffic Jam: Speed Regulation
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Over a road segment, compared to the theoretical speed on that segment the actual speed of vehicles on it is much slower. Ego state data from these vehicles is transmitted automatically to the nearest road side unit (RSU). This is compared statistically and as a result the RSU starts to transmit to the vehicles travelling along the same direction and within the neighbourhood of the segment that there is a traffic jam. At the same time data is uploaded to a central server in order to adapt the speeds of the vehicles leading to that area or proposing alternative routes. Figure 4 shows a schematic representation of this situation. A major question resides on the number of vehicles necessary for such an application to be viable. Floating car data could be available from a limited number of vehicles (SV) as in Figure 4, which could transmit the relevant information to other vehicles. In the figure it is possible to observe that some SV vehicles establish links with other vehicles, those traveling too close to let them know the risk exists. If more precise information is available with respect to a lane in this section of the road, then only vehicles along this lane will be informed, however errors in the estimation of the allocation of the vehicles to a given lane could result on an increase of risk as the information will be transmitted to the wrong vehicles resulting in more congestion or even accidents.
Communications and position information of all vehicles in the road network enable its overall dynamic observability. Therefore, as in any control problem, it could be possible to control the flow and hence optimise its capacity. Sample applications include: Synchronisation of Vehicle Speeds to Traffic Lights. The traversal of long roads controlled by traffic lights could be optimised by synchronising the speed of the vehicles as they reach the traffic lights so as to reduce the brusque speed reductions and thus optimise flow and reduce fuel consumption. Dynamic Platoons. Ensembles of vehicles having cruise control functions could be located in platooning configurations so that they can travel at faster speeds and uniformly. For this purpose, the precise inter vehicle distances and speed information is important as well as certitude of the lane in which the vehicles are travelling. Customised Lane Assignment. Vehicles of a certain class can be restricted or assigned to a lane and their behaviour can also be monitored if necessary. Long haul trucks will be kept on restricted lanes at certain times of the year. Vehicles participating in car pool schemes will be given priority on the use of fast lanes, etc.
Figure 4. Traffic jam notification IV
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Telematics Within the framework of this chapter, Telematics applications comprise the conveying essentially vast information over networks where nodes are static and mobile, that is, information is provided via service and content providers. The points of interaction are infrastructure providers and to some extent Vehicle manufacturers. Figure 5 shows a classical infrastructure for the provision of Telematics services where the consumer is centre on which the vehicle user will accept. The interaction with the vehicle could be made either with the vehicle in steady state conditions
The Localisation Problem in Cooperative Vehicle Applications
Figure 5. A sample structure for telematics applications
or in motion within the transmission range of road side units. In a typical application, there are transactions either service or content which is communicated to the vehicle driver; for this it is sufficient to know that the vehicle is within communication’s reach during the transaction period. At the same time information from the vehicle could be uploaded.
Applications The focus of the applications is transactions of all types where there is the exchange of information and services. It can be triggered at the request of the driver or proposed as the vehicle traverses next to a point of exchange in the infrastructure. In general Telematics related applications operate on transactions or information exchange and do not include data linked to the immediate manoeuvring of the subject vehicle. The degree of position information accuracy is low, as transactions are mainly done on a per zone basis, unlike safety related applications where precise information of the vehicle whereabouts is required. In this section sample applications are only included: Location Based Electronic Commerce. As the vehicle enters a zone a series of services are provided e.g. the purchase of the local tourist guide. This is downloaded upon payment via an electronic wallet. The booking of an event within
the proximity can be made. The transaction is peer to peer and should reduce deployment costs. Downloading of Multimedia Information. Infrastructure points allow for the reception of large amounts of information. This can be in the form of music files, films or specific documents. For this purpose due to the volume involved, it is preferred to be implemented next to road infrastructure elements and perhaps through different RF (Radio Frequency) channels to those used in ITS applications. Updating of Navigation Information. The provision of digital representations of the road network is a fundamental component in ITS applications. It allows for the use of a priori information that is not only used for car navigation purposes but also associated to information captured by the vehicle onboard sensors so as to build models for situation understanding purposes. A major problem today is the relevance and integrity of this stored digital map information, that is, map updating is a major issue and hence is a major Telematics application.
LocaliSAation CONSTtraint in raltrafficENVIironm From the panorama of applications presented in Section 2, it can be observed that it is not sufficient to know the position of the vehicles (with respect to a global navigation reference frame like the WGS 84 geodetic datum) but expressed relative to the road segment travelled by the vehicles. Current car navigation systems function in that manner that is the absolute position of the vehicle estimated by a combination of data from a GPS receiver, odometry, and a gyroscope is related to the spatial representation of digital roadmaps (nodes and links) via a process known as map-matching (Ardeshiri T. et al. 2006). This allows the representation of the location of the vehicle with respect to the context, such as the digital maps.
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In cooperative vehicle applications, this is fundamental as all information shared from other vehicles or infrastructure will be fused in the subject vehicle. That is the common denominator is the spatial representation of the road geometry and its attributes with respect to which any additional information is enriched so as to create a model of the environment that is used for machine interpretable situation awareness for driving conditions. Currently there are two major constraints on the deployment of cooperative applications:
Map Accuracy Maps are networks of nodes connected by links, to which a series of attributes are assigned. Nodes are described spatially with respect to an absolute reference frame to facilitate association with the location estimates. Maps are mainly provided by two companies, namely TeleAtlas and Navteq. In these maps, all types of public traversable roads should be detailed, which are built through several means including terrain surveys, the purchase of existing maps and satellite data. It should be pointed that: Nodes are represented within an accuracy of 15m/48ft for detailed City and Connector Roads and within 100m/328ft for Inter-Town. Only roads known as Advanced Driving Assistance Systems (ADAS) compliant have a spatial description of equal or less than 5m. Further, changes to the road infrastructure have to be reflected in the digital maps. Currently this is difficult and map updates are long to take effect, for example, a year could have transpired before this information could be available to the user. If the map is no longer relevant, the applications relying on it will collapse.
Localisation Estimates The location of a vehicle is estimated based on the sole sources of absolute location information, the Global Navigation Satellite Systems (GNSS),
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like GPS. GNSS based location estimates are calculated by carefully timing the signals sent by the constellation of satellites orbiting around the Earth, which transmit messages based on a common time plus the satellite orbit. In principle, four satellites are used in order to take into account time differences. The accuracy is dependent on the satellite position and signal delay, the latter occurs due to inconsistencies in atmospheric conditions (ionosphere). There are also ephemeris errors, satellite distortion and so on, which will result in errors of the order of 10 to 15m. In urban conditions, as show in Figure 6, considerable errors could exist, which are the results of multipaths (the bouncing of signals from neighboring buildings) or occlusion, that is, there is an insufficient number of visible satellites like in urban canyons. Since the errors can be large as shown in Figure 6, an application might not work normally in such conditions. Figure 6 shows the trajectory of a vehicle recorded using a high-end Inertial Measurement Unit (IMU) coupled to a surveyor level GPS plus vehicle odometry in a loosely coupled manner. The quality of the IMU gyroscopes and acceler-
Figure 6. Response of Automotive type GPS receivers in an Urban Canyon
The Localisation Problem in Cooperative Vehicle Applications
ometers allow for the estimation of the vehicle trajectory with high precision, which is referred to the ground truth, the continuous trajectory in Figure 6. The other trajectories represent the estimates of several COTS low cost GPS receivers recorded at the same time. The response of each is very different and jumps up to 36 m were recorded. The source of errors is mainly due to multipaths and occlusion of GPS signals. To reduce for the lack of GPS signals and compensate errors, the fusion with the vehicle odometers plus gyroscope is made. However when large errors occur or masking is over long periods, this type of fusion is not sufficient, hence, the interest in academia and industry to solve the localisation problem (Lee K. W. 2007 & Toledo-Moreo R. et al. 2007). However, GPS alone is insufficient because estimation errors exists due to position The position calculated by a GPS receiver requires the current time, the position of the satellite and the measured delay of the received signal. The position accuracy is primarily dependent on the satellite position and signal delay.
Localisation: A Combined Problem for Cooperative Vhicles Applications In the previous sections it has been shown that the localisation problem for cooperative vehicle applications is a combined problem as both map and location accuracy introduces constraints. An interpretation of these constraints on the applications can be represented in the study by Guilleron and Konnen and shown in Figure 7 (Gillièron P.Y. & Konnen J. 2003). There is interdependency between the requirements in terms of vehicle location estimations and accuracy of the digital maps, that is, both requirements need to be complied with. Safety: applications dealing with safety depend on the precise localisation of the vehicle relative to the digital map. In this case, map-matching errors with longitudinal and lateral positioning errors being inferior to 5 m are not allowed, particularly in lane level accuracy where sub-meter level accuracy is needed. Mobility: the requirements are less stringent, and tolerated errors are less than 8 m. The main constraint is to ensure that the correct roads are identified and the correspond-
Figure 7. Distribution of localisation accuracy requirements per application domain. (Adapted from Gillièron P.Y. & Konnen J. 2003)
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ing vehicles are associated to them. Efficiency: accuracy in this case should be around the 10 m mark, and the dynamic sharing of information between vehicles should reduce the need for higher accuracy levels. Contrary to the Safety related applications, Mobility and Efficiency applications are concerned with larger numbers of vehicles distributed over large areas and thus the requirements on knowledge of their presence in these areas should be at times sufficient. Telematics: the requirements are different. They should operate within a zone basis and also rely very much on the identification of the vehicle within the application zone. The accuracy should be around 15m. Most applications at the transaction level are mainly large volumes of data.
Analyof SPSpatial n on rntati The spatial and temporal relationships between vehicles and their environment provide indications on whether or not a potential safety risk (collision) might exist. They are used as part of machine inferred situational analysis to trigger different driving assistance functions. Within this context two important parameters could be considered: Reaction time and Manoeuvre constraints. The first takes into account the vehicle overall response time including the driver and vehicle reaction times; it is specific to the time taken by a vehicle to brake. The second considers the ability of the vehicle to change its trajectory by including the vehicle kinematic and dynamic constraints. Most safety related driving assistance functions are based on the ability to warn drivers on time so they can reduce their speed or move to a halt in time. That is to inform drivers of potential hazards with sufficient time that eventually they are able to stop on time or reduce the vehicle speed so as to avoid any safety hazard situation. The same applies to the timely trajectory control that is drivers need to manoeuvre at the correct time
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and the position. Considerations of latencies in the whole process are important as timing is crucial for the relevance of the information. Further, most information is spatial like the relative position of the vehicles between each other or with respect to a road feature like an intersection. Errors in both time and location would render any application of sharing this information across the vehicles and infrastructure, which is no longer possible as uncertainty introduces potential liabilities or safety hazards. To illustrate this section two sample cases are considered: The first addresses the reaction time and the vehicle location as applied to a road intersection assistance application. In this case the dependency of the relative position of the vehicles related to their ability to stop in time is examined. The purpose is to ensure that relevant vehicle(s) stops to allow the respective vehicle to cross the intersection safely. The second case relates to the vehicle location to its trajectory change capabilities. The purpose is to demonstrate the importance of location information with regard to the vehicle context in terms of time and space. In this case, the relevant information has to be sent to the vehicles concerned only in time so as to become applicable. The scenario used is that of a sudden blockage at a motorway exit that implies that the pertinent vehicles within the immediate environment are made aware of the situation so they can take the most appropriate measures, like changing their trajectories.
Road Intersection Assistance The drawing in Figure 8 shows a typical situation that could occur as several vehicles converge towards an intersection. The vehicle running the application is labelled as the Subject Vehicle (SV), while the vehicles involved in the study are the Intruder Vehicle (IV), and the Power Two Wheeler-motor bicycle (PTW). In the scenario, the SV has the right of way and the IV and PTW should respect the stop sign and reach a zero speed
The Localisation Problem in Cooperative Vehicle Applications
before the stop line. If the SV knows the position of the IV, PTW and other vehicles, as well as its own with respect to the intersection, plus their corresponding speeds, it will be possible to know if the vehicles could stop on time before entering the intersection. For this purpose it is possible to estimate the braking distance of the relevant vehicles (IV and PTW) and infer whether or not the vehicles could stop on time. It is assumed that the vehicles’ positions and the digital road map are accurate so it is possible to estimate the vehicle speed and position with respect to the intersection whether or not the vehicles could stop on time when required as in the case of the IV and the PTW. For a V2V or V2I application, a major consideration is the time allocated to interact with the driver so a warning can be sent to the SV drivers on time as they arrive to the intersection. There
are two contextual parameters: One is the Time to Intersection (T2I), which is the interval that the SV or IV or PTW will need to arrive (stop) to the intersection after a given event. The T2I depends on the vehicle initial speed, the environment and the action that the driver will take. The other is the Response time that governs the T2I. That is the timely response to external stimuli from the driver and vehicle. It shows whether it is necessary to look ahead for a given speed and response time (Myopia), and whether the response time is too large for a given speed and look ahead (Latency Problem). A complementary issue is throughput that is guaranteeing adequate sensory and processing throughput. In a stand-alone perception system, this refers to the processing needed in terms of resolution, stabilisation and occlusion compensation (Kelly, A. & Stentz, A., 1998). Within a cooperative vehicle context, it should include delays in the communication process.
DV
Figure 8. Scenario depicting the arrival of several vehicles to an Intersection
VRU
VRU
DV PTW
POV
HTv
IV
SV
S V = su b je ct ve h icle IV = in tru sio n ve h icle D V = d istra cte r ve h icle H T V = h ig h to n n a g e ve h icle V R U = vu ln e ra b le ro a d u se r P T W = p o w e r tw o w h e e l ve h icle POV
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The Localisation Problem in Cooperative Vehicle Applications
The distance to the intersection (D2I) for a stand-alone vehicle relates to the computational reaction time and mechanical manoeuvrability. It is the distance needed to arrive safely (stop or under a controllable speed) to the intersection, as a reaction to observations on the environment. For this purpose, the vehicle dynamics and the spatial extent of both vehicle and the intersection are considered. The response time governs most of the decisions/actions; it can be defined as a perceive-thinkact loop that models the overall vehicle control and planning system. The system response time, is the time period between the instant an object (entity) appears in the field of view (FOV) of the perception sensor and the instant the vehicle has completed the execution of the associate avoidance trajectory. In a cooperative system the FOV will be the electronic horizon (EH) which is larger than the FOV. This time comprises: •
• • •
•
Tperc: perception time, includes detection/sensing the environment, location of the detected object, recognition and understanding Tplan: deciding what to do, which is the action time allocated to the driver Tcom: commanding the actuators, e.g. brakes, throttle, steering wheel Tact: actuator response time, i.e. any time lag between the input command and the actuator response Tveh: vehicle response, associated to the vehicle dynamics.
Tresp = Tperc + Tplan + Tcom + Tact + Tveh
(1)
The total system response time is defined by equation (1): Treact = Tperc + Tplan + Tcom + Tact
(2)
It is possible to distinguish the time before the driver response including the actuator response, from the time after. The former is the reaction time
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whilst the latter is the manoeuvre time, indicated in equations (2) and (3), respectively. Tmanoeuvre = Tveh
(3)
The distance travelled during the reaction time is calculated by the cruising speed and thus linear in initial velocity, whilst the distance travelled during the manoeuvre time will be quadratic, most likely, as the vehicle reaches a zero speed. For cooperative vehicles, if information is received from distributed sources (vehicles and infrastructure), the cooperative system time Tcoop should be added to the response time Tresp. The actuation space of a vehicle is covered by a command vector (wrench) in terms of propulsive rotational effort (heading), propulsive linear effort (speed) and resistive linear effort (deceleration). For the purposes of this analysis, the vehicle is considered as a point object under the assumption that there is an instantaneous response of actuators to their commands. The total travelled distance (Sawo) is defined from the moment an event has been detected, the awareness that an event has occurred (e.g. a warning message received, an object appears within the horizon, an intersection is detected, etc.) to the time the vehicle moves to a halt. It is proportional to the vehicle cruising speed, stopping distance, and driver reaction time. It is the distance travelled by the vehicle during the reaction time Treact times the vehicle cruising speed Vac plus the vehicle braking distance Sbrake. To estimate the braking distance, it is assumed that a full brake command is issued, when the vehicle is travelling at a constant speed Vac in a straight line. The sliding friction coefficient (µ) between the vehicle and ground is taken as equal to 0.65 when the road surface is wet, and equal to 1 when it is dry. If there is a positive slope, the vehicle will stop earlier and if this is negative, the stopping distance will be longer. The road gradient (f) is incorporated and expressed in m/100m, as this affects the braking distance when driving in
The Localisation Problem in Cooperative Vehicle Applications
irregular terrain (this might not be as important when considering intersections). The expression for the Braking Distance will be: Sbrake =
Vac2 2g( ± f )
(4)
Vac2 2g( ± f )
(5)
S AWO = TreactVac +
Therefore, the response or travelled distance is: equation (5). The first term is the reaction distance, and the second the manoeuvre distance. The distances increase in a quadratic manner as a function of the vehicle speed. Therefore, the separation in stopping distances increases at higher Vac , further the effects of Tresp can become predominant. For example, at a velocity of ~40 km/h the difference of 2 s in reaction time implies a brake distance difference of ~22 m; whilst at ~60 km/h the difference is ~33 m. This has implications for cooperative systems as the reaction time is dominated by the whole perception process, communications and human response. A cooperative vehicle solution should not only optimise the communications and sensing component but also the driver reaction, this is a systems problem. A preliminary analysis indicates that the driver should be notified, by considering its reaction times say 2s for any individual and 3s for the elderly. Urban Vehicle speeds are 40 Km/h ±20% and at secondary country roads (the equivalent of the D-type roads in France) are 70 Km/h ±20%. When the vehicle is running in an Urban Setting, the minimum Information distance is IUmin = 28m and the maximum distance is IUmax = 55m. The former occurs when the vehicle is approaching the intersection at a low speed and assumes a fast driver reaction time, whilst the latter assumes that the vehicle is moving at a higher speed and the driver has a slower reaction time. This limits the distances at which information has to be
communicated to the driver. A similar analysis can be made for a rural setting. The simple analysis has shown that even assuming ideal conditions there is a certain degree of uncertainty with respect to the vehicle response and stopping times. However, when applications are safety related, assumptions should be minimal or non existent. On an urban setting where road separation could be as little as, say 20 m, through intelligent map matching and assuming the availability of digital maps with an ADAS type accuracy (5m), it will be possible to discriminate roads travelling parallel to each other if the location estimation is under 5m. The longitudinal error once map matching exists is a different problem. Latencies should exist on the transmitted information. If the vehicle is travelling at 40 km/h and the latency on the receiver data at another vehicle is 200 ms, then the travelled distance will be 2.2m. So if the vehicle needs information at approximately 30 m before the intersection, the latency effects need to be taken into account. If the error on the estimation is 5m longitudinally, the total uncertainty will be 7m. On an urban setting an intersection would could be as small as 10m, thus if a positive error of 7m exist, the vehicle might stop not at the entry point of the intersection but rather in the middle of it. The application based on these assumptions can result in a hazard rather than a driving aid. Localisation has to be resolved locally, that is to associate relative positions to a local model of the environment based on the digital road map, where features are used to improve the vehicle position as landmarks, a kind of assisted simultaneous localisation and map building, as developed in (Lee K. W. 2007).
Lane Change Manoeuvre The drawing in Figure 9 shows a lane change manoeuvre of the SV as it approaches motorway exit junction. The driver could choose to change
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The Localisation Problem in Cooperative Vehicle Applications
Figure 9. Lane change manoeuvres
IV
POV
SV
DV
HTv
DV
to a slow lane and then take the exit junction. An alternative is for the SV to turn left and travel along the fast lane. Both situations are examined next with regard to a V2V driving assistance function. First Case. The SV wants to take the exit junction, thus moves from the middle lane to the slowest lane and then takes the exit junction (blue trajectory in Figure 9). In the SV electronic horizon or digital map, the presence of the motor bicycle (PTW), the IV, the POV, and DVs will be represented as in the drawing assuming that there is a perfect estimation of the positions of the vehicles and the representation of the road geometry. Assuming that the position of the PTW could be estimated with an accuracy of 5m and it is riding close to the middle lane. The width of each of the lanes will be 4m. The PTW position uncertainty is shown in Figure 9, its current position marked using a green link which was matched to the second lane. In this case the electronic horizon will represent wrongly the PTW and thus authorise the SV vehicle change lanes with disastrous consequences. Second Case. The SV wants so move onto the fast lane, A correct representation of all the components in the EH horizon will only be possible if the precision of the location estimates and digital road map are accurate. A critical entity for this manoeuvre is the lane position of the IV vehicle. Again if the position uncertainty is say 5m, the likelihood for the IV vehicle to be
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matched on to the intermediate lane as shown in Figure 9 will be high. Therefore there might be the wrong lane association of the IV vehicle in the digital map and thus for the blind detection/lane assistant application, it will be possible to give the go ahead for the lane change manoeuvre; with unexpected results. This case study shows the importance of good vehicle position estimates. In order to have a good lane discrimination, this needs position errors under 0.5m if safety related estimates are needed. The combination of data from GNSS receivers and proprioceptive sensors alone will not suffice, as this provides mainly an absolute position estimation with errors reaching with much difficulty under 5m in all conditions particularly in urban areas. A different approach is needed that measures relative position of the vehicle and provides sufficient data for a better discrimination like, for example, lane detection that will narrow very much lateral positioning errors and avoid for example the risks associated to the given examples.
CONCLUS ION and DIS CUSS ION This chapter has focused on the constraints imposed by the needs for precise vehicle localisation estimates to be able to deploy vehicles with communications capabilities in real traffic conditions. It has been written from a practical perspective
The Localisation Problem in Cooperative Vehicle Applications
based on the analysis of use cases to infer some results. These reflect the interest on them by major stakeholders in the domain. V2V and V2I safety applications are complex, and the problem does not limit to communications capabilities. It involves spatial information with respect to the vehicles’ locations relative to each other and the infrastructure, and their representation on the only source of common data across the system, feature-rich digital road maps. Time is a very important parameter, it imposes the relevance of the information shared, as scenarios are very dynamic, that is information corresponds to specific instances of time. Information needs to be associated in a spatial manner, for example the location of a vehicle, an obstacle or road works to a lane or road segment. The deployment of advanced functions in the automotive domain is constrained by cost, a major issue for automotive OEMs. The estimation of a vehicle position using low-cost components results on estimates with errors reaching more 10 m in urban conditions. Applications that require that the estimated position should be less than one metre (e.g. need for lane discrimination), implies that their deployment will be difficult using current solutions. Localisation estimates should be relative to the area being traversed by the vehicle and/or with respect to the other vehicles. For this purpose, features on the environment in the form of landmarks need to be used to increase the localisation of the subject vehicle. The availability of beacons like those provided by UWB communications should also contribute to solve this problem. Another constraint resides on accuracy of the digital road maps and their constant update, as all positions are expressed with respect to these maps. Currently only major roads have accuracies less or equal to 5m, because it is economically difficult to extend them to all roads as well as to make them more accurate. Therefore this constraint has to be also included as part of the design of V2V and V2I applications.
Despite the referred constraints there is a strong potential on the use of communications technologies for vehicle safety applications. Constraints will be overcome through the use of embedded machine intelligence as well as complementary exteroceptive and proprioceptive sensors onboard passenger vehicles, bringing opportunities not only for safety applications but also to optimise traffic flow, reduce fuel consumption and pollution. It is likely that within the next future links between infrastructure and vehicles should led to the creation of intelligent spaces, where the sharing of information amongst all actors should be widespread. Each actor has the potential of becoming a sensor providing sufficient information to build digital representations of the world to be used to enhance the drivers’ situational awareness and lead to the deployment of advanced driving assistance functions that may even lead to the autonomous functions.
RE Ardeshiri, T. et al. (2006). Offset Eliminative Map Matching Algorithm for Intersection Active Safety Applications. Proceedings Intelligent Vehicles Symposium IV2006, Tokyo. Arem, B. van, Driel, C. van., & Visser, R. (2006). The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics. IEEE Transactions On Intelligent Transportation Systems, 7(4). Broughton, J., & Walter, L. (2007). Trends in fatal car accidents: Analysis of CCIS Data (Published project report PPR 172). TRL Limiter. Cook, J. A., Kolmanovsky, I. V., McNamara, D., & Prasad, K. V. (2007). Control, Computing, and Communications: Technology for the twenty-first century Model T. Proceedings of
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the IEEE Special Issue Advanced Automobile Technologies, 95(2). Cui, Y. J., & Ge, S. S. (2002). Autonomous vehicle positioning with GPS in Urban Canyon environments, IEEE Transactions in Robotics and Automation, 19. CVIS Project Partners. (2008). Retrieved June 2008, from http://www.cvisproject.org Defense Advanced Research Projects Agency (2008). Retrieved June 2008, from http://www. darpa.mil/grandchallenge/index.asp Department of Transport. (2008). Retrieved June 2008, from http://www.itslessons.its.dot.gov/ Gharavi H., Prasad K. V., & Ioannou P.A. (Eds.), (2007). Special issue Advanced Automobile Technologies. Proceedings IEEE , 95(2). Gillièron, P. Y., & Konnen, J. (2003). Enhanced Navigation System for Road Telematics. In Proceedings the Third Swiss Transport Research Conference, (STR 03), Ascona/Ticino. Kelly, A., & Stentz, A. (1998). Rough Terrain Autonomous Mobility - Part 1: A Theoretical Analysis of Requirements. Autonomous Robots, 5. Laugier, C., Vasquez, D., Yguel M., Fraichard Th. & Aycard O., (2008). Geometric and Bayesian Models for Safe Navigation in Dynamic Environments. Intelligent Service Robotics 1(1). Lee, K. W. (2007). Autonomous vehicle navigation in neighbourhood environments: A map assisted and a feature-based approach. Unpublished doctoral dissertation, Nanyang Technological University.
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Lee, K.W., Wijesoma, S., & Ibanez-Guzman, J. (2007). A constrained SLAM approach to robust and accurate localisation of autonomous ground vehicles. Robotics Autonomous Systems, 55. Madhavan, R., Messina, E. R., & Albus, J. S. (2007). Intelligent Vehicle Systems: A 4D/RCS Approach. New York: Nova Science Publishers. Papp, Z., Brown, C. L., & Bartels, C. (2008). World Modeling for Cooperative Intelligent Vehicles. Proceedings 2008 IEEE Intelligent Vehicles Symposium, IV2008, Eindhoven. SAFESPOT Project Partners. (2008). Retrieved June 2008, from http://www.safespot-eu.org Schlingelhof, M., Betaille, D., Bonnifait, P., & Demaseure, K. (2008). Advanced positioning technologies for co-operative systems. IET Intelligent Transportation Systems, 2(2). Srini, V. P. (2006, December). A vision for supporting Autonomous Navigation in Urban Environments, IEEE Computer. Toledo-Moreo, R. et al. (2007). High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS. IEEE Transactions on Intelligent Transportation Systems, 8(3). Wang, H., Xu, J., Ibañez-Guzmán, J, Jarvis, R., Goh, T., & Chang, C. W. (2000). Real Time Obstacle Detection for AGV Navigation Using Multi-baseline Stereo. In D. Rus & S. Singh (Eds.), International Symposium on Experimental Robotics Lecture Notes in Control and Information Sciences, 271. Springer.
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Chapter XII
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems Denis Gingras Université de Sherbrooke, Canada
ABSTRACT In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. They will then look at the case of robustness of the positioning system when one or some of the sensors are faulty and will describe how the positioning system can be made more robust and adaptive in order to take into account the occurrence of faulty or degraded sensors. Finally, they will go one step further and explore possible architectures for collaborative positioning systems, whereas many vehicles are interacting and exchanging data to improve their own position estimate. The chapter is concluded with some remarks on the future evolution of the field.
INTRODUCTION The field of automotive positioning systems has become a research topic in full rise these last few years. Generally speaking, positioning information in road transportation is of prime importance for safety reasons. We need to know where we
are (vehicle position) and we also need to locate obstacles and other objects/vehicles in the vicinity and nearby environment of our own vehicle. So an approach able to precisely localize our vehicle and evaluate its environment on the road is required to increase safety (Drane & Rizos, 1997). Apart from safety issues, the next generation of vehicles
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
will allow the driver and passengers to have access to a broad range of services, such as path planning, navigation, guidance and tracking, which are based on information technologies, telecommunications and telematics. Future vehicles are likely to be as well mobile offices, information centers on wheel, or e-nodes connected to the web and other networks (Kim, Lee, Choi & al., 2003). To supply those services and to provide the adequate information contents to the driver and passengers, it is required to determine in real-time the vehicle’s position as accurately and efficiently as possible. This function is an essential part of an integrated navigation information system (INIS). An INIS embedded in a vehicle is basically composed of a geographic information system (GIS), a database composed of roadmaps, cartographic and geo-referenced data, a positioning module, a human-machine interface (HMI) as well as computing and telecommunication capabilities. It provides useful functionalities to the driver like path planning, guidance, digital map and points of interest directory (Farrell & Barth, 1999). The guidance module uses a planned trip to indicate the driver which route to take. To avoid giving wrong indications and impair driving safety, the navigation system relies upon a positioning module to know precisely and continuously the localization of the vehicle. In this chapter, we will focus on the positioning module of INISs. Nowadays, the most popular sensor used for positioning is a global positioning system (GPS), also called a GPS receiver. However, GPS alone based systems suffer from various problems such as poor data latency, signal multipath errors and occlusions of satellite signals. To alleviate these problems and obtain the required performance of the positioning module, we usually use a combination of heterogeneous and complementary sensors whose measurements are integrated or fused in real time (Cannon, Nayak & al., 2001, Grewal, Weill & Andrews, 2001). Among the other types of sensors, we have Distance Measurement Indicators (DMI), such as differential odometers,
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and 2 or 3 axis inertial measurement units (IMU). Two or more of these complementary positioning sensing methods must be integrated together to achieve the required performance at low cost. The data integration, which implies the fusion of noisy signals provided by each sensor, must be performed in some optimal manner. Basically the goal of data fusion in positioning systems is 1) to fill in the time gaps whenever we face a loss of position estimate and 2) to improve the position estimate by ensemble averaging and exploiting information redundancy. Historically, data fusion for positioning has been applied in robotics (Roumeliotis & Bekey, 2000, Durant-Whyte, 1994) as well as in military, space and avionic applications (Suresh & Sivan, 2004). Multi-sensor data fusion system design based on rapid prototyping software platform and Kalman filtering for land navigation has also been proposed (Abuhadrous, Nashashibi, Laurgeau & Chinchole, 2004, Redmill, Martin & Özgüner, 2000). Hence, with the advent of more powerful, accessible and cheaper technologies embedded in vehicles for road transportation, onboard navigation systems are becoming feasible and standard in the automotive industry. The chapter is constructed as follow. After the present introduction, we will look at the specific problem of vehicular positioning and localization, what are the systems and sensors available to do it and what are the problems to be solved. In the following section, we will consider the most common data fusion architectures when multiple positioning sensors are used. Positioning system designers usually choose the Kalman filter or a variation thereof as the data fusion method. We will then look at the various implementations possible, such as the extended Kalman filter (EKF), the unscented Kalman filter (UKF) (StPierre & Gingras, 2005) and particle filters (PFs) (Arulampalam, Maskell & al., 2002), which are generalizations or variations of the Kalman filter used to cope with nonlinearities of the sensors or with non-gaussianity of the signals. To complete the survey, we will also present an interesting
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
alternative to classical Kalman filtering based on the use of artificial neural networks (NNs) (St-Pierre & Gingras, 2004). After having surveyed those various data fusion approaches and their respective performances, we will then focus our discussion on the reliability and robustness requirements for a real time positioning system. In positioning navigation systems, at any time, any of the sensors can break down or stop sending information properly, temporarily or permanently. To ensure a practical solution for use in guidance and navigation systems, faulty sensors must be detected and isolated such that their erroneous data will not corrupt the global position estimates. As we just mentioned, Kalman filter is usually popular for data fusion applications. However, an interesting idea is to use it for fault detection architecture as well. We will then evaluate the potential of combining fault detection and data fusion into a single architecture to make a robust positioning navigation system (Guopei & Gingras, 2007). In the subsequent section, we will then consider the possibility to achieve a distributed collaborative architecture. Proliferation of real time inter-vehicular communications provides new sources of exploitable positioning data. Vehicles can, under numerous situations, have GPS satellite shortages but there will always be vehicles in their vicinity, viewing different GPS satellites line of sights (LOS), to provide them with useful navigation information. We will thus look at a cooperative positioning technique making use of reliable positions of some vehicles to enhance positioning estimates of some others (Abdessamie & Gingras, 2008). The approach, based on a simple idea, exploits inter-vehicle data flow to extract good position measurements from vehicles with good GPS satellite LOS, in order to enhance low positioning accuracy of other vehicles, in the neighborhood. The integration of such information is done using geometric data fusion approach. Finally, we conclude the chapter with a discussion on future trends in the field, what are the chal-
lenges ahead and what can be expected for future positioning systems. Some general comments are provided along with proper references.
CONTEXT AND PROBLEM SATEMENT Modern automotive navigation applications require position estimates with a precision of 1 meters or less and at a frequency of 1 Hertz or more. Basically three types of positioning architecture can be used to locate a vehicle in its environment (Laneurit, Blanc & al., 2003) The first type involves absolute localization, that is, a global position of the vehicle is given in the world reference by some exteroceptive sensors such as a GPS receiver. The performance obtained often depends on satellites visibility and actual satellites configuration at the time of measurement. Good satellites configuration are one which present a sufficient elevation angle, a good signal to noise ratio and a good geometrical configuration of the satellites. A differential GPS receiver alone could in principle estimate the position with the required performance. However, it is still much too expensive for automotive applications and the fixed references are not always available. On the other hand, a low cost GPS receiver cannot always achieve the required precision and reliability as it suffers multipath signal propagation and frequent signal outages, most specifically in urban areas. The second type involves relative localization, that is, when the position and orientation of the vehicle are determined from its previous position. Integrative sensors, like odometers and inertial systems, are commonly used in relative positioning. This type of measurement, however, involves time derivation. We therefore observe a rapid divergence of the localization when distance increases due to cumulative errors in the estimate. This is the main drawback of this family of sensors. Two popular sensors used are the differential
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odometer and the inertial measurement unit (IMU) (Abbott & Powell, 1999). A differential odometer measures the distance traveled by a vehicle and the current azimuth during a sampling period. An inertial measurement unit (IMU) measures the vehicle’s inertia characterized by its acceleration and its angular velocity. An IMU measures the acceleration and the angular velocity along the axis of a Cartesian coordinate system. With these two sensors, the position of the vehicle is reckoned by applying basic kinematics’ equations and using an initial position obtained from another information source. By means of integration, the traveled distance and the azimuth variation can be computed and therefore a new position can be obtained with these measurements and the last known position by dead reckoning. The estimated position will eventually drift from the real position because of the accumulation of errors. Indeed, the recursive nature of the positioning computation with the IMU causes the positioning error to grow proportionally with time. Typically, DMIs (odometer/IMU) can cumulate a drift in the position estimate of the order of 10m to 15m after ten minutes of integration. A periodic reset of those relative localization systems is therefore needed. The third type of positioning architecture consists of a hybrid form of localization, that is, this method combines the two previous ones in order to cumulate their advantages and to alleviate their drawbacks. The third type, which we will consider from now on, implies data fusion from the various sensors. A trivial way to fuse the data from these two sensors is therefore to reset periodically the IMU position estimate with an absolute position estimate from the GPS. A more complex fusion method than reckon/reset positioning system described above is required to improve the precision of the estimation. These methods fuse continuously the available measurements in some optimal sense, as depicted in figure 1 in a centralized architecture.
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Figure 1. Centralized data fusion architecture GPS IMU Odometer
Fusion Method
Estimated Position
Inclinometer
One important issue is data registration and latency correction. Odometer and INS signals are received with a nearly constant latency (this error is usually far less than lms). However, GPS positioning calculation time and reception delay may vary largely. Fortunately, it is possible to calculate those delays using the Pulse Per Second GPS signal and introduce them in the data fusion algorithms. When the latency of the GPS data is low, the position information can be propagated forward using the velocity data and a constant vehicle velocity model. This is usually adequate for slow moving vehicles. Problems occur, however, when the latency of the GPS data is high and the vehicle’s dynamic is fast. For example, if the GPS latency is 0.2s and the vehicle is travelling at 30 m/s (110 km/hr), then the vehicle will move six meters ahead while storing the IMU data. If the vehicle is moving at constant speed following a straight path, then the position estimate can simply be adjusted forward accordingly. However, any curve or any deviation from a straight line will lead to incorrect position estimates. Simple straight GPS correction is not sufficient in that case and a more sophisticated latency correction algorithm is required before applying and fusing the GPS data with the current inertial state. The sensors’ measurements are also distorted by deterministic and random errors. Those sources of random errors are usually described using stochastic models in a statistical framework. The estimate ideally maximizes the a posteriori probability of the random variable position, resulting from the mathematical transformation of the
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
stochastic processes, which model the sensors’ imperfect measurements. The fusion method then defines the mathematical transformation. The architecture of data fusion system for positioning can be decentralized or centralized. In the centralized architecture, all the sensor measurements are fused by one fusion method only. So it is easy to compare the performance of two different fusion methods when the cluster of sensors is the same. Before looking into the details of data fusion architectures, we will now look at the different sensors available for localization.
SENSORS MODELS In a positioning navigation system, various sensors are used. Generally speaking, a sensor is a device that responds to or detects a physical quantity and transmits the resulting signal to a controller. Positioning sensors can be designed to detect various parameters (coordinate, distance, direction, or angular velocity) of the position of vehicular mechanical systems. Sensors can provide absolute or relative navigation information. GPS and magnetic compass provide absolute position and angular direction (azimuth) respectively, whereas all other sensors provide relative
navigation information using dead reckoning. Detailed relationships between the position and typical sensors are shown in Table 1.
GPS Model The GPS model can be described by equation (1) (Farrell & Barth, 1999). ˆ = ρ[t] + δtrop[t] + δiono[t] + δwhite[t] + δmult[t] (1) ρ[t] ˆ is the pseudo-range measured by the where ρ[t] emulated receiver, ρ[t] is the real pseudo-range, δtrop[t] is the tropospheric delay, δiono[t] is the ionospheric delay, δwhite[t] is the white noise generated by the receiver’s electronic components and δmult[t] is the multipath problem.
IMU Model The 3-axis inertial measurement unit has a single model for every gyroscope and accelerometer described by equation (2), (Grewal, Weill & Andrews, 2001). dˆi [t ] = di [t ] + si (di [t ] + d j [t ]*sin(δij ) t
+ d k [t ]*sin(δik )) + bi [0] + ƒ wbi [n] n =1
Table 1. Relationship of vehicle position and sensor outputs Sensor
Output
Relation to position
Rover position
Directly output position coordinates
IMU
Accelerations and angular rate
Outputs can be integrated by an INS to obtain the vehicle position
Odometer
Distance or increment of distance
Inclinometer
Inclination
Magnetic compass
Azimuth
GPS
Position coordinates are determined by dead reckoning from the distance and direction relative to a known location
+ d j [t ]*sin(δij ) + d k [t ]*sin(δik ) +ci (di [t ] + d j [t ]*sin(δij ) + d k [t ]*sin(δik )) 2 + wi [t ],
(2)
where dˆi [t ] is the measurement, di[t]is the real value, si is the scale factor error, dj[t] is the real value relative to the axis j, dk[t] is the real value relative to the axis k, δij is the misalignment angle between the axis i and j, δik is the misalignment angle between the axis i and k, bi[0] is the turnon bias, wbi[n] is the random walk white noise characterizing the bias drift, ci is the non-linear
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scale factor and wi[t] is the additive white noise component. The scale factor error, the bias parameters, the non-linear scale factor and the additive white noise component have different values depending on the sensor type.
Differential Odometer Model DMIs consist of wheel rotation sensors, such as differential odometers and ABS (anti-breaking systems) sensors (Bonnifait, Bouron, Meizel, & Crubille, 2003). Those are the one usually used in automotive localization systems. The wheel revolutions are integrated from them to measure the total distance traveled by the vehicle. Given time information traveled, a forward velocity is determined. As opposed to GPS receivers, DMIs are not subject to signal masking or outages. However the positioning errors cumulate with time, as is the case for the IMUs. A differential odometer is constituted of two sensors measuring the number of rotation of each wheel situated on the same axle. The total traveled distance and the azimuth of the vehicle can be computed with the equations (3) and (4) respectively. dˆ[t ] = dˆ[t − 1] +
(1 + vd * v[t ] + sd ) * cd [t ] + r [t ] 2 (1 + vg * v[t ] + sg ) * cg [t ] + r[t ] 2
+
(3)
θˆ [t ] = θˆ [t − 1] +
(1 + vg * v[t ] + sg ) * cg [t ] + r [t ]
− l (1 + vd * v[t ] + sd ) * cd [t ] + r[t ] , l
(4)
ˆ is the total traveled distance at time t, where d[t] ˆ – 1] is the total traveled distance at time t-1, d[t v[t] is the car’s velocity, vd and vg are the gains
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characterizing the tire dilatation, sd and sg are the scale factors for the right and the left wheel respectively, cd[t] and cg[t] are the numbers of rotations measured within the interval [t-1,t] for the right and left wheel respectively, r[t] is a uniform random variable describing the resoluˆ is the azimuth estimated at time t, tion error, θ[t] ˆθ[t – 1] is the azimuth estimated at time t-1 and l is the axle length.
Data fuSIion TECHNIQUES The Kalman Filter The most basic data fusion method for localization is based on the Kalman filter (Kalman, 1960). It is a set of mathematical equations that provides an efficient computational means to estimate the state of a process, in a way that minimizes the mean square error. This filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. The Kalman filter is an optimal linear estimator, which uses the a priori information on the sensor noise sources, the vehicle dynamic and the kinematics equations to compute recursively an optimal position, while minimizing the mean square error (Gelb, 1974). The filter is optimal when the process noise and the measurement noise can be modeled by white Gaussian process. The recursive implementation of Kalman filters is well suited to the fusion of data from different sources at different times in a statistically optimal manner. Many other filter designs can be shown to be equivalent to the Kalman filter, given several constraints. The recursive sequence involves prediction and update steps. The prediction step used a dynamics model that describes the relationship between variables over time. A statistical model of this dynamic process is also necessary. A prediction is usually done to estimate the variables at the
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
time of each measurement, as well as in between measurements when an estimate is required. The measurement update combines the historical data passed through the dynamics model with the new information in an optimal fashion. The Kalman filter addresses the general problem of trying to estimate the state x ≠ R n of a discrete-time controlled process that is governed by the linear stochastic difference equation We then have, xk + 1|k = Φk + 1|k[xk|k] + wk
(5)
zk + 1 = Hk + 1|k[xk + 1|k] + vk + 1,
(6)
where xk + 1|k is the predicted process state vector, xk|k is the estimated process state vector, Φk + 1|k is the discrete state transition matrix from k to k+1, wk is the process noise vector, vk + 1 is the measurement vector, Hk + 1|k is the observation matrix and vk + 1 is the measurement noise vector. Supplied with initial conditions PO and xO, the prediction equations can be given by xˆk−+1 = φk xˆk
(7)
Pk−+1 = φk Pk φTk + Q k
(8)
and the update equations are given by K k = Pk− H kT (H k Pk− H kT + Rk )
(9)
xˆk = xˆk− + K (zk − H k xˆk− )
(10)
Pk = (I − K k H k )Pk− ,
(11)
−1
Where xˆ is the vector of estimated states, P is the covariance of estimated states, Q is the dynamics noise matrix, K is the Kalman gain matrix, R is the covariance matrix and z is the vector of observations. Although an error state Kalman filter is desirable in some applications, a state space model will be preferred here as it most clearly illustrates the operation of the filter. Rigorous development of
a Kalman filter requires a great deal of work in understanding the physics and electronics involved in each sensor to understand what the error sources will be. A comprehensive model of all of the necessary variables to match as closely as possible the real world phenomenon is needed. Given the specifications of the project, a sensitivity analysis would then be done to decide which variables may be ignored or lumped together. This often involves Monte Carlo simulations with several likely candidate filters and repeated tuning of the statistics. The filter must be able to operate with the allowed throughput and processing restrictions. Finally, blunder detection, adaptive filter gains, and practical limits to covariance must be set to achieve optimum performance. The Centralized Kalman Filter (CKF) is the most common filter design implemented in integrated navigation systems. However, it behaves poorly in the presence of nonlinearities. In the following sections, we will look into more details of the use of the various improvements possible over the basic Kalman filter for an integrated navigation information system.
The Extended Kalman Filter Improvement of the basic Kalman filter architecture can be achieved in nonlinear situations with the extended Kalman filter (EKF) (Anderson B. & Moore J.,1979, Brown R. G. & Hwang P. C., 1997). This filter is based upon the principle of linearizing the state transition matrix and the observation matrix with Taylor series expansions. The extended Kalman filter has been very popular for land navigation system (Abbott & Powell, 1999, Wu, Gao & Wan, 2002, Redmill, Kitajima & Özgüner, 2001). The equations of a centralized data fusion architecture based on an extended Kalman filter for land navigation positioning system are described in (Abbott & Powell, 1999). As with the Kalman filter, the extended Kalman filter predicts the states of the random process using equation (5). The predicted states are updated with the measurements in equation (6). 225
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Box 1.
aR cos(Φ y )*cos(Φ p ) − sin(Φ y ) cos(Φ y ) *sin(Φ p ) aN … ⁄ … aP ⁄⁄ = … sin(Φ y ) *cos(Φ p ) cos(Φ y ) sin(Φ y ) *sin(Φ p ) ⁄ * … aE ⁄⁄ (12) … … ⁄ … … − sin(Φ p ) 0 cos(Φ p ) aY ⁄ … aD ⁄
In our study presented in (St-Pierre & Gingras, 2005), we have 13 states to describe the random process. A position-velocity-acceleration model is used for each component of the position (Brown & Hwang, 1997). The last four states include the slope, the pitch, the azimuth and the yaw velocities. The state transition matrix Φk + 1|k is linear. Only the observation matrix Hk + 1|k contains nonlinear equations, the most relevant for horizontal positioning is described by equation (12). (See Box 1), where aR, aP, aY are the acceleration vector components along the roll, the pitch and the yaw axis respectively, Φp and Φy are the Euler angles for the pitch and the yaw axis respectively, aN, aE, aD are the acceleration along the north axis, the east axis and the down axis respectively. The extended Kalman filter approximates the nonlinear matrix H based on the Taylor series expanded about the estimated state vector with:
H [ xˆk +1 k ] ≈ H [ xˆk k ] +
∂H [ xˆk k ] ∂xˆk k
( xˆk +1 k − xˆk k )
(13)
The linear approximation often introduces large errors in the estimated state vector and can lead to the divergence of the filter. As expected, the linearization can lead to poor performance and divergence of the filter for highly non-linear problems. In addition, the performance analysis of the extended Kalman filter presents some difficulties due to the recurrence of the measure sequence into the states of the filter (Grewal, Weill & Andrews, 2001). Finally, implementa-
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tion of the extended filter can be quite laborious depending on the number of states required to model the system. For all these reasons, a recent improvement to the EKF, named the “unscented” Kalman filter (UKF) has been proposed (Julier & Uhlmann, 1997).
The Unscented Kalman Filter The UKF approximates the posterior probability density resulting from the nonlinear transformation of a random variable instead of approximating the nonlinear functions with a Taylor series expansion. The approximation is done by evaluating the nonlinear function with a minimal set of carefully chosen sample points using a deterministic sampling approach. The posterior mean and covariance estimated from the sample points are accurate to the second order for any nonlinearity (Merwe, Freitas & Wan, 2000). If the priori random variable is Gaussian, the posterior mean and covariance are accurate to the third order for any nonlinearity (Wan & Merwe, 2000). The first use of an unscented Kalman filter for land navigation positioning system is described in (Julier, 1997). Apart from our work (St-Pierre & Gingras, 2005), one paper has been recently written on the use of the unscented Kalman filter as the fusion method in an integrated navigation information system (Li & Leung, 2003). An unscented Kalman filter has also been used for GPS positioning (Mao, Wada & al., 2003). The unscented Kalman filter is based on the unscented transformation, which is a method for reckoning the statistics of a random variable
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
undergoing a nonlinear transformation. A set of 2*nx+ 1 weighted samples are deterministically chosen to capture the true mean and variance of the prior random variable. nχ = nx + nw + nv
(14)
where nx is the number of process states, nw is the dimension of wk and nv is the dimension of vk. The unscented Kalman filter approximates the nonlinear observation matrix by 2*nχ
H [ xˆk +1 k ] ≈ ƒ Wi * H [χix,k +1 k ] + χiv,k +1 (15) i =0
2*nχ
ƒ
ˆk +1are H [ xW Wi * H [χi ,k +1 k ]are + χthe ] ≈the weights, Where, i , k +1sigma i k i =0 2*nχ points describing the prior predicted states and Wi * H [χix,k +1 k ] + χiv,k +1 are the sigma points describing the meai =0 surement noise. In order to obtain statistically reliable data on the performance of both algorithms, one hundred Monte Carlo simulations have been run for each sensor fusion method (St-Pierre & Gingras, 2005). For each sampling time, the estimated positions from the Monte Carlo simulations form the sampling distribution. There were 26639 measurement vectors for each Monte Carlo simulation. These sampling distributions approximate the truth continuous distributions of the posteriori random variables describing the estimated positions. The first moment of each sampling distribution has been computed and used for the computation of the performance metrics. The two performance metrics usually encountered in data fusion systems analysis are the accuracy/precision of the fusion and the computational time to perform the fusion. The accuracy is evaluated by taking the Euclidian distance between the estimated position and the true position. The mean and the variance of the Euclidian distances for the whole simulation are reckoned. The variance describes the precision of the fusion method. The horizontal position is described by the tangential plane located at the
ƒ
x
v
real vehicle position whose coordinates are given by the latitude and the longitude. In (St-Pierre & Gingras, 2005) we gave detailed results showing that the unscented Kalman filter has slightly better results for horizontal positioning than the extended Kalman filter. The estimated position is less biased for the unscented Kalman filter than for the extended Kalman filter. It shows also that the unscented Kalman filter is more precise than the extended Kalman filter. However, contrary to the claim in (Merwe, Freitas & Wan, 2000), (Wan & Merwe, 2000), we found that the computational cost of the unscented Kalman filter is significantly greater, by a factor larger than 20, than the one of the extended Kalman filter. The significant execution time difference is related to the number of times equations (5) and (6) are evaluated for each fusion algorithm. With the unscented Kalman filter, these equations are evaluated 75 times, once for each sigma point. With the extended Kalman filter, the Taylor series expansion of these equations is evaluated only once at each iteration. Furthermore, the Jacobian of the matrix H used in the Taylor series expansion is calculated only once because the observation equations are static. Thus the multiple computations of equations (5) and (6) by the unscented Kalman filter at each iteration is responsible for the larger computational cost. Surprisingly, we found that the unscented Kalman filter is less performing than the extended Kalman filter when there is no GPS solution available (St-Pierre & Gingras, 2005). In that situation, the acceleration of the vehicle measured by the IMU is used to estimate the vehicle’s position described by equation (12). This equation represents the nonlinear transformation of the estimated states which are assumed to be Gaussian random variable in order to predict the IMU measurement. The performance of both filters depends on their capacity to estimate the mean of the resulting random variable. An empirical analysis has been made to evaluate this capacity. In this experiment, each state has been modeled by a discrete Gaussian
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
random variable with 100 realizations distributed uniformly in the range of possible values with a 99% probability of realization. Each realization is present a number of times proportional to its probability of realization in the statistical data representing the probability function. Thus, 24060 samples modeled each random variable. The nonlinear function described by equation (12) is then applied to these random variables and the means of the resulting random variables are computed. The same discrete random variables have been used with the Taylor series expansion of equations (12) and (13). In the extended Kalman filter, the linearization occurs around the states estimated at the previous iteration. The linearized equation is applied to the predicted states at the current time. The linearization error is directly proportional to the difference between the estimated states and the predicted states. For the empirical analysis, the mean variation between the estimated value and the predicted value obtained with the extended Kalman filter for one Monte Carlo simulation has been taken. Table 2 shows the percentage variation between the real mean of the a posteriori probability density and the estimated mean of the a posteriori probability density obtained with the Taylor series expansion and the unscented Kalman filter respectively. As can be seen, the unscented Kalman filter provides no significant improvement over the extended Kalman filter and even brings some degradation in performance for two acceleration components (roll and pitch). The superiority of the unscented Kalman filter happens only when the variation between the predicted states and the estimated states is Table 2. Difference between the real mean and the estimated mean of the a posteriori density Estimated state
EKF
UKF
Roll acceleration
0.0064 %
0.8070 %
Pitch acceleration
0.0218 %
1.2876 %
Yaw acceleration
0.2482 %
0.0754 %
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important. However, due to the low dynamics of the vehicle, most of this variation is not important enough to generate a significant linearization error for the EKF.
The Particle Filter More recently, a new class of sequential stochastic methods (Monte Carlo) for Bayesian filtering, called particle filters (PF), has been proposed to solve the multi-sensor fusion problem in land navigation, tracking and positioning systems (Caron F, Davy M. & al. in press, Arulampalam M. S., Maskell S. & al., 2002). Generally speaking, Kalman filters are special cases of Bayesian filters suitable to estimate posterior probability distributions when linearity or gaussianity are assumed. However, this is not always a valid hypothesis. The usual standard approach consists of making model simplifications or crude analytic approximations to obtain algorithms that can be easily implemented. However these simplifications bring undesired behaviour of the systems as we have seen with the Kalman filter in real situation. With the recent availability of high-powered computers, numerical-simulation based approaches can now be considered and the full complexity of real problems can be addressed. Although these integration and/or optimization problems can be tackled using analytic approximation techniques (ex. EKF) or deterministic numerical integration/ optimization methods (ex. UKF) these classical methods are often not sufficiently precise or robust, or they are simply too complex to implement. In these situations, Monte Carlo algorithms become an attractive alternative. These algorithms are remarkably flexible and extremely powerful (Doucet A. & Wang X. 2005). The basic idea is to draw a large number of samples (named particles here) distributed according to some probability distributions of interest so as to obtain consistent simulation based estimates. As opposed to the Kalman filter based algorithms, those particle filters are more general and
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
do not assume Gaussian posterior probability distributions (Andrieu & Doucet 2002). In general, those distributions have a very complex shape, and cannot be calculated in closed-form as it requires integration and optimization of complex multidimensional functions. They can be arbitrary multimodal or highly skewed posterior probability distributions. This kind of distributions may arise in positioning systems when faced with nonlinear observations, evolution models or non-Gaussian noise sources. This occur for instance in the presence of sensor failures or abrupt changes in sensors’ functioning conditions. Particle filters or sequential Monte Carlo methods for Bayesian filtering provide a numerical approximation of those posterior and marginal posterior probability distributions using a set of weighted random samples, called particles. Particle filters can deal with hybrid (both continuous and discrete). state vectors. In most cases, the trade-off between estimation accuracy and computational burden comes down to adjusting the number of particles used. The standard particle filter algorithm is based on sequential importance sampling, with weight normalization. A detailed mathematical description, of the standard particle filter algorithm can be found in (Caron, Davy. & al., 2007) and (Doucet & Wang, 2005). Results and performances reported in the literature (see references above) so far show that significant improvements can be achieved in general dynamic systems, where non Gaussian posterior distribution and nonlinearities have to be dealt with (Crisan & Doucet, 2002).
Data Fusion Using Neural Networks A centralized fusion usually implied the use of only one algorithm, which fuses all the measurements provided by the sensors. The engineering labour required to construct a centralized Kalman or particle filter can be very high. For this reason, most of the GPS/INS systems have decentralized filters, which gain in simplicity. However, the
price to pay is a loss of precision compared with a centralized architecture (Knight, 1997). An interesting alternative is to use a neural network (NN) for the data fusion. Indeed, a centralized NN is no more difficult to realize than a decentralized one. NNs have already been applied to data fusion related to positioning problem in robotic with success (Kobayashi & Arai, 1998, Beattie, 1998, Hagan and Menhaj, 1994). The major advantage of an NN compared to a standard Kalman filter resides in the fact that it does not need any a priori statistical and mathematical model to find a function, which maps optimally the inputs with the outputs, in our case the absolute position of the vehicle. The most difficult task however, is to gather sufficient training sensor data, which can cover adequately the different manoeuvres to be encountered by a road vehicle and its dynamic. The manoeuvres are a function of the road geometry and the vehicle’s performance. For example, a NN trained with measured data coming only from a straight road segment may not be able to give a good position when the vehicle meets a curve. As an example, we will consider the architecture described in more details in (St-Pierre & Gingras, 2004). We used a feed-forward backpropagation neural network that was trained with 14939 training data set for 2000 epochs. The NN has 4 layers composed of 20, 20, 20 and 3 neurons respectively. The corresponding transfer functions are linear, log-sigmoid, tan-sigmoid and linear. This architecture was the most promising of the 12 architectures investigated with various numbers of layers, transfer functions and number of neurons trained initially for 100 epochs. Batch training was preferred over iterative training for its computation efficiency. The mean square error gave the training performance at each epoch. The second order training method was the scaled conjugate gradient. This method requires only O(N) operations per epoch compared to others methods like Gauss-Newton O(N3) and Levenberg-Marquardt O(N3) (LeCunn & Bottou, 2001, Hagan & Menhaj, 1994). When considering the
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Table 3. contains the NN’s inputs Data
Sensor
Latitude
GPS
Longitude
GPS
Altitude Percent Dilution Of Precision (PDOP)
Table 4. Mean of the positioning errors by the Kalman Filter and the NN Position
Kalman (m)
ANN (m)
Improvement (%)
GPS
Latitude
2.73
9.34
-242.90
GPS
Longitude
12.44
3.56
71.38
Altitude
49.21
0.38
100.78
Linear acceleration
INS
Horizontal centripetal acceleration
INS
Vertical centripetal acceleration
INS
Yaw angular velocity
INS
Table 5. Variance of the positioning errors by the Kalman filter and the NN Position
Kalman
ANN
Improvement (%)
Pitch angular velocity
INS
Latitude
1073.2
606.1
43.52
Total traveled distance
Differential odometer
Longitude
994.8
1029.7
-3.51
Azimuth
Differential odometer
Altitude
12952.0
2.4
99.98
GPS solution availability
GPS
Sampling time
IMU
network size, the other training methods were impractical. The GPS receiver sampling frequency is usually lower than those of the inertial measurement unit or the differential odometer. For synchronization purpose, a Boolean entry specifies to the NN if a GPS solution is available or not. A centralized Kalman filter has also been realized as a reference for the comparative evaluation of the NNs performances. The centralized Kalman filter has 13 states and 10 measurements. The simulation generated 26430 data samples. The mean and variance of the positioning error during the simulation was computed. Table 4 indicates that the positions estimated by the NN are less biased for the longitude and altitude but more biased for the latitude than the same positions estimated by the Kalman filter. The NNs estimation biases do not exceed 10 meters. So even if the NN is a biased estimator, it still meets the required performance. As shown in Table 5, the variances of the latitude errors and the altitude errors for the NN are less than those of the Kalman filter. The variance
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of the longitude errors is 5 percent more for the NN than for the Kalman filter. In our particular case, the performance of the NN is generally better than the performance of the Kalman filter in a mean square error sense. The important improvement for the altitude is caused by the NNs ability to estimate the bias of the GPS altitude. It can be seen here that NNs can be used as a centralized fusion method. The results show that neural networks may be an attractive alternative to the Kalman filter as a centralized fusion method. In (St-Pierre & Gingras, 2004), we showed also how NNs can also be used prior to fusion as nonlinear pre-processing filters for the land navigation positioning problem. Similar use of NNs was applied successfully to telecommunications problems (Chuah & Sharif, 2001). In our paper, we showed the NNs capability to successfully learn nonlinear functions when applied to GPS and differential odometer measurements. The major difficulty with an NN is to have access to sufficient ground truth data for the supervised training. Usually, the only solution is to have access to some data coming from a reference, usually given by high precision and high cost sensors. Some further research on this topic would include the evaluation of vari-
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
ous NNs with real sensors and the replacement of the feed-forward back-propagation NN with other types of NN such as recurrent NNs. More exhaustive comparisons with other data fusion techniques are also required.
ROB AND ADAPTIVE POS ITION ESIMATE IN PRESENCE OF SERS FAULTS So far, we considered mainly the case of continuously operating noisy sensors. But what happen to the data fusion process and the position estimate if one of the sensors fails? Either from a user’s safety point of view or a designer’s perspective, all automotive navigation systems should be fully reliable and prevent faults or failures. In all but the most trivial cases the existence of a fault may lead to situations with safety, health, environmental, financial or legal implications. Although good design practice tries to minimize the occurrence of faults and failures, it is recognized that such events do occur. In such cases, faulty sensors must be detected and the system must be able to reconfigure itself so as to overcome the deficiency caused by the fault. In brief, a navigation system must be robust and adaptive. Faults can cause the loss of the overall performance of a system, which may present hazards to personnel or lead to unacceptable economic loss. In order to minimize the impact, fault detection schemes must be developed. In addition to the potential use of particle filters described in the
previous section, several other fruitful research efforts in the field of fault detection and filter based adaptive architectures, combining fault detection and data fusion, have been proposed to improve the reliability and adaptability of various control systems (Patton, Frank & Clark, 1989, Pouliezos & Stavrakakis, 1994, Isermann, 2005). However, little has been published so far in the area of automotive navigation systems.
Fault Detection Architecture A fault is usually defined as an undesired change in system estimated parameters that degrade partial or overall performance. Fault detection is a binary decision making process. Either the system is functioning properly, or there is a fault present. Generally speaking, fault detection consists of two processes: residual generation and decision making, as shown in Figure 2 (Hashimoto, Kawashima & al. 2001, Magrabi & Gibbens, 2000).
Residual Generation Residuals are defined as the resulting differences between analytically redundant quantities in the system model. These are similar to innovations generated by a Kalman filter, which are the differences between the measured and estimated outputs. Under normal conditions, residuals are small or zero mean; while the occurrence of a fault causes the residuals to go to non-zero or unusually large values.
S ens or Outputs
Figure 2. Fault detection architecture
R es idual G eneration
Decis ion Making
no fault fault
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Decision Making The decision making process, which acts as an arbitrator, involves assessing the residuals and identifying when and where any abnormalities occur. This is done through threshold testing both static and dynamic residual behaviors, and various statistical tests, where the thresholds are typically based on signal/residual variance.
S So far, although the precision and reliability of sensors are improved significantly with the development of the technology, various sensor faults driven by different situations do exist. In the following, several faulty scenarios of sensors are investigated and discussed.
Global Positioning System (GPS) Faulty Model A low-cost GPS receiver can output the vehicle position and driving speed. However, the measurement is likely to be corrupted by time-correlated noise and the GPS signal is susceptible to jamming. However, the position and velocity measurements do not drift over long periods of time. A GPS faulty model can be based on four particular parts: typical error budget, environmental interferences, signal loss, and hardware malfunction. Table 6. GPS Error sources and their approximate Deviation [27] Source of errors
Standard deviation (m)
Common Ionosphere
7.0
Clock and ephemeris
3.6
Troposphere
0.7
Non-common
232
Receiver noise
0.1~0.7
Multipath
0.1~5.0
Typical Error Budget The main error sources in GPS are listed in Table 6. These errors can be divided into two categories (Farrell &Barth, 1999, Kronander, 2004): common and non-common. Common errors are approximately the same for receivers operating within a limited geographic region. Non-common errors are unique to each receiver and depend on the receiver type and multipath mitigation technique being used (if any). The point of this classification is that DGPS can effectively remove the common errors. Environmental Interferences GPS satellite signals, as with any other radio signals, are subject to some forms of interference and jamming. It is known that GPS satellite currently transmit position information in the 1,500-MHz frequency band with a typical accuracy under 100 meters to anyone in the world who has a simple receiver costing as little as $100. Any electronic systems generating radio signals in this frequency band, main lobe or side lobe, will tend to be a source of inference to the GPS receiver. With the popularization of personal radio and Wi-Fi devices, electromagnetic interferences, intentional or unintentional, are more and more serious. As an example, the proliferation of ultra-wideband (UWB) devices intended to be mass-marketed to the public could cause harmful interference to GPS. Signal Loss GPS is a line-of-sight sensor, and therefore GPS measurements are subject to signal outages. If it cannot “see” four satellites, then it will not produce the expected output. This case is called signal loss. It may include the following scenarios: • • •
Urban environments with all buildings (the so-called urban canyons). Inside parking structures. In a long tunnel without any relay station.
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Figure 3. GPS faulty model Random noise
Real world
• •
Environmental interference
Typical error source
Under heavy foliage. Under bridges.
Hardware Malfunction A GPS receiver hardware malfunction can be caused by any abnormality of its components, such as antenna, amplifier, reference oscillator, frequency synthesizer, wire disconnection, and power lost, resulting to no output, or provide an unstable or incorrect signal. Compared to the other sources of GPS faults, the probability of a hardware malfunction of the GPS receiver is rather low and can be considered negligible. Taking all the above into account, the GPS faulty model can be described as in Figure 3. Inertial Measurement Unit (IMU) Faulty Model A low-cost IMU can output the vehicle accelerations and angular rate which can then be integrated by an Inertial Navigation System (INS) to obtain the vehicle position, velocity, and attitude. The advantage of an INS is low sensitivity to highfrequency noise and external conditions. But the measurement error of INS will accumulate if it is not calibrated on-line. Several IMU error models have been derived (Kim, Lee, Choi & al., 2003), which are all equivalent in the Kalman filtering context. Error state vectors consist of navigation parameters, and accelerometer and gyroscope error states (Jekeli, 2001). In this model, the errors
Signal loss
Position information GPS receiver
modulated by the Earth’s spin rate are neglected because of short-range nature of our present application. In this paper, the error or faulty model used is presented in figure 4 and the scenarios driving the IMU to a faulty state are discussed below: IMU Error Sources and Faulty Scenarios For this sensor, we find the following sources: •
• •
Bias due to bearing torques (for momentum wheel types), drive excitation feedthrough, electronics offsets and environmental temperature fluctuations. Intuitively, bias is any nonzero sensor output when the input is zero. Scale factor error, often resulting from aging or manufacturing tolerances. Alignment errors: Most stand-alone IMU implementations include an initial transient period for alignment of the gimbals (for gimbaled systems) or attitude direction cosines (for strapdown systems) with respect to the navigation axes. Errors remaining at the end of this period are the alignment errors. These include tilts and azimuth reference errors. Tilt errors introduce acceleration errors through the miscalculation of gravitational acceleration, and these propagate primarily as Schuler oscillations plus a non-zero-mean position error approximately equal to the tilt
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Figure 4. IMU faulty model diagram
Real world
Alignment error
Scale factor error
Quantization error
Nonlinearity error
• • •
error in radians times the radius from the earth center. Initial azimuth errors primarily rotate the system trajectory about the starting point, but there are secondary effects due to coriolis accelerations and excitation of Schuler oscillations. Cross coupling error (non-linearity). Quantization error, which is inherent in all digitized systems. Fault due to one or multiple of the moving parts wear out or jam, or gimbals lock.
The IMU faulty model can be illustrated as in Figure 4.
Odometer Faulty Model An odometer is one of the most common devices used for tracking and to provide relative positioning information of vehicles. In the transmissionbased odometer, the distance to be determined is based on the number of counts for the wheel and calibration constants which are proportional to the radius of the tire. Thus any potential trends that change the radius and the number of counts can drive the odometer to a faulty output. Hence, in term of calibrating the odometer, the scale factor error is the most critical element, as it affects the total distance traveled and the forward speed of the vehicle. Hence, adequate modeling for this error state could be either a random constant or a first order Gauss-Markov process.
234
IMU
Tire Radius Change The detailed sources of tire radius variation are listed in the following reference (Lezniak, Lewis & McMillen, 1977). Mainly: •
•
•
Tire radius tends to increase as vehicle velocity increases because of increasing centrifugal force on the tire. Tire radius tends to increase as air pressure within the tire increases due to increase tire temperature or other factors. Tire radius tends to increase as tread is worn off during the lifetime of the tire.
Road Situation These kinds of error sources depend on the road situation, including: •
•
Running over objects on the road, slips or skids involving one or more wheels when the vehicle accelerates or decelerates too rapidly or travels on a snowy, icy, or wet road. In sharp turns, the contact point between each wheel and the road can change, so that the actual distance between the left and right wheels will be different from the one used to derive the heading.
Gears Tooth Lost An odometer can operate by counting the pass of teeth or tabs of the ferrous wheel mounted on the
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
rotating shaft of the vehicle. If one or more teeth are lost, then the value will abate n/t, where n is the number of the lost teeth, t is the total teeth of the wheel. In the real situation, the occurrences of losing three or more teeth are so puny that they can be omitted. In brief, the wear out of the tire, the pressure of the tire, the velocity of the vehicle, the slippage of the tire, and the gear teeth lost will all contribute to the imperfect operation of the odometer.
Compass operations include the following error sources (Zhao, 1997): •
•
Inclinometer Faulty Model The error sources of the inclinometer may consist of: •
•
•
•
Error caused by thermal expansion or temperature changes. A normally distribution band-limited white noise is used to demonstrate the thermal noise. Drift, calibration error or quantization error due to analog to digital converter resolution. Electromagnetic interference (the major component of the inclinometer faulty model). It can be a uniform or a Gaussian distribution, or a combination of both. The variance and amplitude depend on the traveling environment. Power lost or hardware malfunction: A permanent fault, but since it is only in a very low possibility, it is omitted in this simulation.
Magnetic Compass Faulty Model (Fluxgate Compass) A magnetic compass is an inexpensive absolute direction sensor. The main drawback with this device is that the quantity measured, i.e. the intensity and direction of the magnetic field, can be distorted in the presence of metals and other electrical or magnetic fields, such as power lines, transformers and cars’ powertrain systems.
•
•
Hilly road error: When the vehicle is traveling over a hilly road, the compass plan will not be parallel to the plane of the Earth surface. The compass measures only the projection of the vector components. This is a short-term magnetic anomaly. Random noise error: a) In the situation of traveling nearby power lines, big trucks, steel structures (such as freeway underpasses and tunnels), reinforced concrete buildings, or bridges (short-term magnetic anomalies); b) In an environment of electrical or magnetic noise, or magnetization of the vehicle body (long-term magnetic anomalies). Calibration error: Misalignment of the compass with respect to the vehicle frame simply results in a constant error. This type of error can also be attributed to an inaccurate estimation of the current declination. Permanent fault: power lost or interface cable disconnected (very low possibility, they are being omitted in the simulation).
Adaptive and Robust Data Fusion Architecture In positioning navigation systems, high precision and reliability with low cost are always pursued. Actually, for road navigation, the benefits of the information obtained by the fusion process make it possible to use multiple less powerful, lower cost sensors to achieve as good a performance as those much more expensive ones. Kalman filter and its derivatives, the most popular data fusion methods, have been used extensively in autonomous or assisted navigation system for several years. But almost all of these applications are based on the assumption that all sensed data are complete and reliable. If anyone or more sensors are faulty, then the fusion filters
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Figure 5. Adaptive sensor fusion system
Fault Detection Process
Gaussian noise
Sensors
measurements
Fusion Process
will tend to malfunction as well. In order to ensure a reliable positioning estimate in the case of faulty sensors, an adaptive approach is proposed as in Figure 5. As most of the navigation systems, the objective of the above system is to make the position estimate of the vehicle as accurate and reliable as possible. Sensors (GPS, IMU, odometer, inclinometer and compass) data which are used to compute the position and attitude of the vehicle, often involve sources of uncertainties. Meanwhile, a state space model can be constructed from the vehicle dynamic to perform the function of sensor fusion. Both their outputs (measurements and estimates) can be combined together through a particular function so as to generate a residual signal. Passing this signal through a detection process, a decision is made: either the system is running properly or there is a fault occurring, which leads to the fusion process rerunning to optimize the position estimates. From the diagram above, we can see how a residual signal generator and a fault detector are embedded into the conventional data fusion architecture. A Kalman filter approach is used, since it is simpler and more effective to attain the residual signal via state estimation. The key idea is to reconstruct the outputs of the process with the aid of a Kalman filter and to use the estimation error, or some particular statistical functions of them to assess the residual signal.
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residuals
Residual Generator
estimates
output
Then the faultiness can be detected by considering the residual properties against some threshold. If any information from the sensors is detected to be faulty (residual properties go over a given threshold), then the corresponding measurements are discarded. In that case, the position estimate needs to be re-updated. In this approach, fault detection and data fusion are combined into a single Kalman architecture to construct a fault tolerance, robust and adaptable vehicle positioning system. The detailed flow chart is shown in Figure 6. Note that the shaded blocks are part of the fault detection process. Detailed tests results presented in (Guopei & Gingras, 2007) show that the proposed Kalman filter based state estimation scheme ensures that the position estimate is always optimal and brings significant benefit to the data fusion system compare with the conventional fusion architecture without fault detection, in particular for the frequent GPS signal loss case. Performance analysis and more details on this architecture can be found in (Guopei & Gingras, 2007).
COLLABORATIVE DATA FUSION S So far we have considered the case of a single vehicle only. In real life however, cars are not alone on the road. We may therefore ask ourselves
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Figure 6. Adaptive sensor fusion architecture flow chart Initialize xˆk− and Pk−
Compute the Kalman gain
K k = Pk− H kT ( H k Pk− H kT + Rk ) −1
Update estimate with measurement zk xˆk = xˆk− + K(zk − Hk xˆk− )
Residual generation r=
z k − H k xˆ k
z k2 + ( z k − H k xˆ k )
2
Decision making ri > Threshold ?
Y
Discard ith measurement Ki = 0
N
Update the error covariance Pk = (I − K k H k )Pk−
Project the error covariance ahead Pk−+1 = φ k Pk φ kT + Qk
Project the state ahead
xˆ k−+1 = φ k xˆ k
Next iteration
the following questions: can we take advantage of the other vehicles proximity and positioning data to improve our own position and, if yes, how? The intelligent vehicle systems (IVS) envisioned here would be able to communicate their position and navigation information through inter-vehicle communications (IVC). IVS will be evolving in
mobile ad hoc networks called (MANET) that give access to valuable real time data, especially high precision positioning information. However, in terms of global navigation systems (GNS), individual vehicles in a given MANET would not have access to the same constellation of satellites. And certainly vehicles with good lines of
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Figure 7. Collaborative positioning architecture: In this simplified example, the vehicle at far right and at far left have a good line of sight (LOS) and can share and provide their good GPS position estimate to the vehicle in the center who has a bad LOS and therefore a poor GPS position estimate. All vehicles are assumed equipped with GPS receivers
sight (LOS) have more precise position estimates than vehicles with a poor LOS. Moreover, some vehicles may possess high precision DGPS or beacon-based positioning information to share in the network. In this section, we briefly present a collaborative positioning concept that uses some of the above IVC features, along with additional range measurement capabilities, to ameliorate positioning estimates of neighboring vehicles in a MANET (Abdessamie & Gingras, 2008). Two vehicles at different locations can have different sets of visible satellites and by mean of inter-vehicle communications, the satellite information can be shared between vehicles (Berefelt, Boberg & al., 2004). Many research activities are being conducted in IVS collaborations. We ref to (Nirupama, 2005) for
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a good introduction to IVS research and development. For a little more technical reference on the subject and still worth reading, see (Jurgen, 1998). Designing a collaborative system for accurate estimation of relative positions of neighboring vehicles based on real-time exchange of individual GPS coordinates, while using vehicle-to-vehicle radio communications is a challenging task (Kukshya, Krishnan & Kellum, 2005). Research in the domain of collaborative navigation was usually aimed for collaborative driving systems (CDS), where car platoons or mobile robots are involved (Gaubert, Beauregard & al, 2003, Crawford & Cannon, 2005). However, most CDS previously reported require either costly vehicle-to-vehicle relative dependence or vehicle-to-infrastructure dependence.
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Figure 8. Principle of geometric data fusion approach in collaborative positioning of vehicles using GPS as shown in Figure 7. The far right and far left vehicles are sharing their good GPS position estimates using appropriate fusion in order to reduce the uncertainty ellipse of the vehicle in the center of the platoon
In a recent paper (Abdessamie & Gingras, 2008) we have proposed a less expensive navigation approach, exploiting existing positioning resources embedded in vehicles and independent of road infrastructures. To illustrate the concept here, we will treat only the case of three vehicles, as shown in figure 7, which, although limited, is sufficient to understand the underlying principle in a more general case. In order to consider the design of collaborative localization architectures, such as the one we present in (Abdessamie & Gingras, 2008), we need to consider a set of various assumptions in order to simplify the model. In our case, we supposed the following conditions to be valid: •
•
All vehicles in the MANET are equipped with necessary navigation items (GPS, DR, IVC sensors…etc.) All vehicles have range measurement radars, to provide precise inter-vehicle distance.
•
•
• • •
•
Millimeter wave radars MMW for automotive, studied in [3], would be appropriate solution to our application for they have a high Doppler sensitivity and a 200 meters range with a good precision which is very suitable for our case. No vehicle dynamics are considered in the CPA, we instead used a simple motion model. Error covariance matrix on position, heading, and inter-vehicle distance contains the global errors of vehicle systems. No special vehicle frame is considered Coordinate reference system is geodetic altitude, latitude and azimuth. No altitude difference is considered, this corresponds to the case where all three vehicles are located on a relatively flat plane with a constant altitude. Inter-vehicle communications are real time and safe.
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An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
Collaborative Uncertainty Minimization In a distributive, collaborative approach, each vehicle has its own set of position estimates as well as other information such as range information from other neighboring vehicles. To achieve a collective improvement of position estimates, we have considered a geometric data fusion approach (Abdessamie & Gingras, 2008). This method is based on the geometric analysis of the sensing uncertainty and is motivated by the geometric idea that the volume of the uncertainty ellipsoid should be minimized, as illustrated in figure 8. The resulting fusion equations coincide with those obtained by Bayesian inference, Kalman filter theory, and by weighted least-squares estimation (Abidi & Gonzales, 1992). When systematic error or bias is made small, the uncertainty ellipsoid encloses a region in space where the true value most likely exists. The center of the ellipsoid is the mean of the measurement and the ellipsoid boundary represents a distance of one standard deviation from the mean. Geometric data fusion has been used in many research applications (Elliott, Langlois & Croft, 2001, Lee & Ha, 2001). It had proven to be a powerful uncertainty management data fusion technique. In (Abdessamie & Gingras, 2008), we showed that a collaborative approach can be used in navigation systems and further improve the position estimate over a conventional data fusion approach. More mathematical details and performance analysis can be found in the paper.
Concluding rmark and futurtr nd In this paper, we have surveyed the positioning estimation problem applied to land navigation systems and reviewed some of the various sensor fusion techniques usually encountered in such systems. We have discussed their relative
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performance and limitations. The extended Kalman filter (EKF) in data fusion centralized architectures remains a design of choice for most applications. We have explained how to make such systems more robust by detecting and identifying sensor faults. Finally we looked at the possibility to exploit the presence of several vehicles in the vicinity, in order to improve one’s own position estimate using a collaborative and geometric data fusion approach. A main trend can be seen in the field of dense sensor networks, where multilateration can render good location accuracy despite significant errors in range estimates between sensors. It can therefore be useful for vehicle navigation. This has launched a research area known as sensor location using wireless telecommunication techniques, which seeks to process potentially enormous quantities of data collectively to achieve optimal positioning results. Some papers (Biswas & Ye 2004, Hightower & Boriello, 2001) provide good surveys of available techniques for sensor location. Usually, the techniques achieving the best performance process the input data in a centralized fashion; however distributed versions are also available. Savvides, Park & Srivastava (2002) solve a global nonlinear optimization problem for localizing sensors through Kalman filtering, which yields good results, but requires a high number of anchor nodes. The multi-dimensional scaling technique employed by (Shang, Rumi & al., 2003) for sensor location achieves good results with few anchor nodes, but requires high radio connectivity. In (Savarese, Rabaey & Beutel, 2001) good results are presented, based on an architecture employing a two-phase initiation and refinement approach, and, as opposed to the aforementioned papers, deals with relatively high levels of noise. According to results previously reported, the algorithms achieving the best performance in sensor location techniques use quadratic constraints to minimize a linear objective function (Biswas & Ye, 2004, Doherty, Pister & Ghaoui, 2001, Hightower & Boriello, 2001). Since some of
An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems
the constraints are non-convex, the papers above differ primarily in their relaxation approaches to render the problem convex. According to those publications, the solution provided by Biswas et al. (2004) has greater applicability and yields better results than the one by Doherty & al. (2001). The paper by Gentile, (2005) follows their same centralized approach, maintaining the efficiency of convex optimization. However by applying linear triangle inequality constraints as opposed to quadratic ones, the problem is automatically convex. As a result, a tighter solution is guaranteed since no relaxation of the constraints is needed. With the current evolution of automotive technologies, embedded positioning systems will become more and more easily available at a lower cost, thus allowing all vehicles to be equipped with such technologies. In addition, all vehicles are becoming networked and equipped with wireless communication capabilities, thus allowing the use of distributed and collaborative techniques for navigation and positioning. Wireless communications networks are becoming attractive to localize vehicles using various radio-based range technologies such as received signal strength indicators (RSSI), power signal attenuation or time-of-arrival (TOA) techniques (Parker & Shahrokh, 2006, Kukshya, Krishnan & Kellum, 2005, Chan, Tsui, So & Ching, 2006, Tsukamoto, Fujii, Itami. & Itoh, 2001, Qi, Kobayashi & Suda, 2006). Originally, most existing radio-based ranging technologies for position estimation has been applied to fixed wireless sensor networks, because it is not possible, for practical and economic reasons, to equip all sensor nodes in a network with GPS receivers. Previously proposed radio-based ranging approaches have been either distributed or centralized, providing a more or less precise location estimate of the nodes, depending on the scheme used (also called course grain and fine grain schemes). Course grain localization, usually configured in a centralized fashion, requires less radio connectivity between nodes but needs a central processing node in the
network to establish localization of the remaining nodes. It works fine only if a rough estimate of a node’s position is sufficient (Doherty, Ghaoui, & Pister, 2001). Conversely, fine grain localization requires more resources and radio traffic, but provides a relatively accurate node position estimates. Fine grain localization may operate in either a distributed or centralized approach. The main challenges in using these techniques for vehicular applications, originally developed for fixed sensor networks, are related to the continuous high mobility of the nodes. In a more theoretical context, extensions of the Bayesian framework, which copes with the different types of imperfections of the various sensors and sources of information, such as imprecision, uncertainty, incompleteness, and ignorance, have also been proposed for the data fusion problem in vehicle localization. For example, Fisher’s fiducial probability, fuzzy logic, credal inference, Demster’s belief functions and transferable belief model (TBM) are being used and described in various papers, such as (Caron & Smets, 2005, Caron, Duflos & al., 2006, Smets & Ristic, 2004) to name a few. Finally, in a more global context, the advent of new satellite networks for navigation in the years to come, such as GALILEO in Europe, are likely to boost vehicle localization worldwide capabilities and provide more flexibility in the design of future positioning systems for automotive applications (Vejrazka, 2007). Clearly, more robust, networked and distributed intelligent positioning systems are likely to be encountered in future positioning and navigation systems.
AcknowlEDdgmnt I would like to thank the Canadian NCE AUTO21 for funding this research project, the University of Calgary for providing the acceleration data, Geomatic Canada for the topographical information and E. A. Wan, A. D. R. Merwe and OGI
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School of Sciences for the Rebel Toolkit. I would like also to thank my former students Mathieu St-Pierre, Guopei Liu and Rabah Abdessamie for their original contributions in this area.
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Chapter XIII
Efficient and Reliable Pseudonymous Authentication Giorgio Calandriello Politecnico di Torino, Italy Antonio Lioy Politecnico di Torino, Italy
ABSTRACT Privacy, security, and reliability are key requirements in deploying vehicular ad-hoc networks (VANET). Without those the VANET technology won’t be suitable for market diffusion. In this chapter, the authors are concerned with how to fulfill these requirements by using pseudonym-based authentication, designing security schemes that don’t endanger transport safety while maintaining low overhead. At the same time our design improves the system usability by allowing nodes to self-generate their own pseudonyms.
INTRODUCTION Vehicular ad-hoc networks (VANET) are composed by mobile nodes, that is, vehicles, and road-side units (RSU) equipped with sensing, processing and communication capabilities, and are deployed to increase road safety and the efficiency of transportation systems through a number of applications, most of which will be based on beacons, that is, vehicles disseminating frequently their position, state and eventual situation-dependent warnings (for example, ice on the road).
Such applications are sensible to attacks, for example by injecting false beacons into the network, and can disclose users’ personal information or track vehicles, as an attacker collecting VANET data could do. Thus there’s the need of security mechanisms and privacy-enabling technologies, which actually are a prerequisite for deployment. Three recent research efforts, the IEEE 1609.2 working group (IEEE1609.2, 2006), the NoW project (Gerlach et al., 2007), and the SeVeCom project (Papadimitratos, Buttyan, Hubaux, Kargl, Kung & Raya, 2007), are currently working on VANET security architectures, whose common
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Efficient and Reliable Pseudonymous Authentication
mechanism is the use of public key cryptography to protect V2V and V2I messages. Message authentication, integrity, and non-repudiation, as well as protection of private user information are identified as primary requirements. On one hand, traditional public key cryptography in combination with periodic key changing is known as pseudonymous authentication, in which nodes are equipped with multiple credentials, termed pseudonyms that do not reveal the node real identity, e.g., as those proposed by Chaum (1985). This prevents messages signed under different pseudonyms to be linked (Armknecht et al., 2007; Raya & Hubaux, 2005; Gerlach, 2005; Raya & Papadimitratos, 2006). On the other hand, group signatures, that is, cryptographic primitives for anonymous authentication, are also applicable to VANET (Ateniese & Tsudik, 1999; Brickell et al., 2004; Chaum & van Heyst, 1991; Syverson & Stubblebine, 1999). This guarantees a stronger level of anonymity as an eavesdropper cannot link any two signatures from any node. This property comes at a higher security overhead as it will be explained in the rest of the chapter, while pseudonym-based authentication gathered a warmer and wider acceptance by the research community (Gerlach et al., 2007; IEEE1609.2, 2006; Papadimitratos, Buttyan, Hubaux, Kargl, Kung & Raya, 2007), reason for which our focus in this chapter is on pseudonym-based systems. In this chapter we investigate technical issues related to the use of pseudonyms in vehicular communications. First, we ask ourselves how the security overhead can be reduced while maintaining acceptable levels of security and application robustness. Second, do safety applications remain reliable in presence of security mechanisms? Finally, can self-generation of pseudonyms still meet the security and privacy mechanisms? In this chapter, we provide answers to these questions. First we define more precisely the problem at hand and we outline our approach to solve it. Then we analyze the costs of the proposed solutions and their impact on transportation safety. 248
BACKGROUND We present the idea of self-generation of pseudonyms in our previous work (Calandriello et al., 2007). Zeng (2006) presents the same approach independent of our work, and Armknecht et al. (2007) apply it to VANET. Calandriello et al. (2007) show that the proposed optimizations can also be applied to the cryptosystem presented by Zeng (2006). The work of Raya & Hubaux (2005) presents the main problems and challenges of VANET security, and outlines in detail the Baseline scheme which we summarize in the following. Papadimitratos, Gligor & Hubaux (2006) survey the security requirements of VANET and provide models for the system and the adversaries. We invite the non-specialized reader to refer to the above works for an in-depth introduction to VANET security. Chaum (1985) presents the original idea of using pseudonyms, while their applicability to VANET has been explored by other studies in the context of the SeVeCom and NoW projects: (Gerlach, 2005; Gerlach et al., 2007; Papadimitratos, Buttyan, Hubaux, Kargl, Kung & Raya, 2007; Raya & Hubaux, 2005). Papadimitratos, Kung, Hubaux & Kargl (2006) discuss in general the issue of privacy in vehicular communications. A number of works are concerned with different aspects of security and privacy of vehicular networks, either outlining challenges (El Zarki et al., 2002; Parno & Perrig, 2005), describing particular attacks (Blum & Eskandarian, 2004; Jakobsson et al., 2004) or more general attack overviews (Aijaz et al., 2005), offering general suggestions towards solutions (Gerlach, 2005; Raya, Papadimitratos & Hubaux, 2006), or proposing mechanisms (Golle et al., 2004; IEEE1609.2, 2006; Li et al., 2005; Raya & Hubaux, 2005). The work of Laberteaux & Hu (2006) proposes to authenticate messages through the combined use of public and symmetric key cryptography. Freudiger et al. (2007) approach the problem
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privacy in vehicular networks from another perspective, proposing the use of mix-zones to change pseudonyms, and analyze the privacy level achieved. Many recent works propose the use of bilinear pairings to provide privacy in VANET. Lin et al., (2007) use group signatures directly on VANET messages, as we survey in the Group Signature approach below. We discuss the advantages and disadvantages of this scheme later in the paper. The approach from Lu et al., (2008) is similar to ours (Hybrid) but heavily relies on the infrastructure to generate pseudonym, which is a requirement hardly achievable in practice. Torrent-Moreno, Jiang & Hartenstein (2004), Torrent-Moreno, Corroy, Schmidt-Eisenlohr & Hartenstein (2006), Chen et al. (2007) extensively analyze the VANET channel conditions, providing metrics to measure the channel performance. Chen et al. (2007) provide extensions to the ns-2 simulator to use realistic propagation models and to reliable assess the channel conditions. TorrentMoreno, Santi & Hartenstein (2006) provide a distributed algorithm to fairly share the available bandwidth by effectively adjusting the beacon power level. On the safety application side, Xu et al. (2004), Yang et al. (2004), Biswas et al. (2006), ElBatt et al. (2006) propose schemes for safety messaging and analyze their impact with respect to delay and transportation safety, although they do not consider the costs of security. Our previous work (Papadimitratos, Calandriello, Hubaux & Lioy, 2008) analyzes the impact of security on transportation safety.
SAFETY AND SECURITY REQIREMENTS Vehicular communications primarily aim at enforcing transportation safety. This means saving thousands of lives otherwise lost in accidents and saving pollution, time and fuel waste due to traffic jams and unorganized traffic.
Applications for transportation safety are real-time: they require low delay in message transmission and processing as emergencies happen suddenly and drivers have to react fast in order to avoid troubles. Human reflexes are in the order of magnitude of 1 s. Safety applications should be much faster (e.g. one order of magnitude), both on communication and processing sides, in order to allow the driver to take quick response to an emergency event. Their goal is to disseminate alerts fast, in order for the driver to be warned much earlier than he is nowadays, without such applications. Current envisioned safety protocols (Biswas et al., 2006; ElBatt et al., 2006; Xu et al., 2004; Yang et al., 2004) rely on consecutive broadcast retransmissions of the emergency warning, and are essentially based on redundancy, with optimizations designed to reduce the channel load and to extend the message dissemination area beyond the range of a given transmitter. It’s important that the communications overhead is kept low: in a scenario with high node density every vehicle will contribute to saturate the channel, and this will affect the performance of safety applications, for example by increasing the transmission delay. Each vehicle will contribute regardless of the content of the messages it send; essentially, the offered load depends on the beaconing rate, on the payload size and on the security overhead. While the former depends on the specific application, the latter is directly affected by the security design; given its relevance, it’s then critical that it’s kept to a minimum. As these are life-critical applications they must be reliable, both in terms of fault-tolerance and security, otherwise the system becomes useless. Safety applications must be trusted as they can endanger drivers’ life, and in general the system must be secured to guarantee its efficiency and avoid a formidable set of possible abuses. On the networking side, this means that a vehicle must trust the messages it receives. Consider, for example, nodes that “contaminate” large portions
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of the network with false information: a single vehicle can transmit false hazard warnings (e.g. ice formation on the pavement), which can then be taken up by all vehicles in both traffic streams. Or, similarly, a vehicle that meaningfully modifies messages of other vehicles, or even a vehicle that forges messages in order to masquerade as an emergency vehicle to mislead other vehicles to slow down and yield. These simple examples of exploits indicate that vehicular communications must be secured: otherwise these systems could make anti-social and criminal behavior easier, in ways that would actually jeopardize the benefits of their deployment. Thus, a comprehensive set of security mechanisms integrated into the VC system is critical for their deployment (Papadimitratos, Gligor & Hubaux, 2006). We outline here a subset of requirements which apply to safety messaging: Message authentication and integrity. Messages must be protected from any alteration and the receiver of a message must be able to verify that the sender of the message is a legitimate node of the network. Message non-repudiation. The sender of the message cannot deny having sent a message Privacy and anonymity. Vehicle communication systems should not disclose or allow interference on the personal and private information of their users. This requirement is becoming increasingly important as is the sensitivity of personal information. In VANET, examples of “personal information” are the most frequent areas where a car is parked, or the most used paths between two different points. If no privacy protection mechanism is in place, any (passive) eavesdropper could obtain such information and connect them to the driver’s real identity, becoming thus able to track her at any given point in time and space. This Big Brother scenario is easier to achieve than one could imagine, as it only needs a series of receivers placed in key locations and enough time to collect the needed information; in fact, without privacy protection, any single message from a given vehicle will reveal its identity. 250
In particular we require anonymity for the actions (e.g. messages or transactions) of the network nodes with respect to a set of observers. At best we require full anonymity: an action performed by a given node could have been performed by any other node in the system. Full anonymity can be however a drawback in some circumstances as well, for example during an emergency where it would be desirable to track a sender of emergency messages in order to facilitate the management of alerts. As full anonymity is extremely costly to achieve, as it will be explained in the following of the chapter, we soften this requirement to limited traceability: a node can be tracked and its messages linked, but only for a limited amount of time. We emphasize that this does not automatically mean that the node is identified, that is, its long-term identity disclosed. This latter requirement allows for a node to be tracked during an emergency and thus to better manage the stream of alerts. Liability identification. Users of vehicles are liable for their deliberate or accidental actions that disrupt the operation of the network, and the latter should help in the attribution of liability, for example by providing meaningful information. This is a requirement that implies that anonymity could be removed and the node identified if necessary. It’s a matter of policy to specify the observer with the legal power to do so.
EXAMPLE SCENARIO We outline an example scenario to show why the above security requirements are important. Let’s assume a vehicle V moving from two locations A and B. As V moves, it receives beacons and alerts from other vehicles. V needs some mechanism to trust the content of the received messages, otherwise the system would not be usable at all. Without security, any pedestrian, car, base station enabled with the DSRC protocol can send messages, and there would be absolutely no way to
Efficient and Reliable Pseudonymous Authentication
tell true warnings to false ones. Without security, V could be for example forced to change route because it receives fake alerts of “traffic jam ahead”. As explained above, messages need to be authenticated, and their integrity ensured, in order to avoid false messages and alerts. Let’s now imagine that two other vehicles in the radio range of V have an accident. As they are VC-enabled, they would send messages up to the moment of crash, which V can store and reveal during later investigation. Some other mechanism is then needed to tell which message was sent by which vehicle, otherwise the investigation would be useless. This mechanism would guarantee the property of non-repudiation. Let’s now imagine that someone is eavesdropping on the network. Without security, it can learn that V is on the street at a given moment, match it with the owner data, learn for example that A is the V’s home and B its workplace, and when the house is empty.
ADVERSARY MODEL We characterize the adversary with 2 dimensions: geographic extension and method, with the former being local or global and the latter active or passive. We note that we do not attempt to define all possible adversaries and attacks against VANET, but we limit ourselves to a subset which targets the security requirements specified above. The first adversary to consider is the global passive, thus an eavesdropper with the ability to overhear messages on a “significant” portion of the network, that is, on a geographic area wide enough for the eavesdropper to collect meaningful information, and attempting of recovering the drivers’ personal data. The second model is the active adversary, either local or global, which controls a radio transceiver and can thus inject messages on the network. This adversary is a threat for safety applications as it disseminates bogus information on the network,
for example “car crash ahead, slow down”, when such event did not occur. If such information is picked up by the nearby vehicles the attacker take over the functionality of the VANET safety system and cause accidents or traffic jams. To continue the previous example, nearby vehicles will most likely abruptly slow down, starting a potential dangerous situation. Both types of adversaries do not have access to legitimate credentials or, in other words, we choose not to consider internal adversaries. Such choice implies that vehicles are physically protected against tampering, and that such protection is adequate, at least against the high majority of users.
SYSTEM MODEL VANET employ the Dedicated Short Range Communications (DSRC) data link technology (DSRC, 2006), according to which nodes use one common channel to transmit beacons, that is, periodic updates about their state. Beacons enable safety applications, and their rate ρ range from 3 to 10 per second, with the latter value deemed mandatory; this is also the value we assume in the following of the chapter. Beacon payload includes at minimum the position and the speed of the vehicle, as well as a timestamp and eventually different warnings. Safety applications run on each vehicle and identify dangerous situations from the beacon content. Their response is to send warnings to other vehicles and alerts to the driver, but they will not override the latter. We assume that alerts, when transmitted, are part of the beacon payload. The timestamp combined with digital signatures also gives protection from replay attacks. For the scope of this chapter, we don’t consider multi-hop routing and focus on broadcast transmissions only. Finally we consider vehicle-initiated transmissions only, as fixed infrastructure nodes don’t require their privacy to be protected.
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We assume that each vehicle has a long-term, unique identity that combines a number of attributes (Papadimitratos, Gligor & Hubaux, 2006) and a set of cryptographic keys. We assume for simplicity one global and unique Certification Authority (CA) managing credentials with which all legitimate nodes are registered, and with the legal power to revoke credentials and disclose identities of the nodes, if deemed necessary for liability attribution.
PROB AND APPROACH OVERVIEW We want to secure VANET by fulfilling the requirements described above. Regarding message unlinkability, we require that any two messages m1, m2, generated at times t1, t2 respectively cannot be linked if t2 > t1 + τ, where τ is a user-specified parameter. This means in practice switching to a new set of keys every τ seconds, and that only signatures generated with the same key can be linked to each other. This is the basic idea behind pseudonymous authentication, and τ is termed the pseudonym lifetime. Our first proposal, in order to simplify the credentials management, is to allow vehicles to self-generate their own pseudonyms and the corresponding private keys. This eliminates the need of pre-loading, storing, refilling, as well as obtaining pseudonyms over an “out-of-band” channel, and improves the system usability, as vehicle’s privacy is not compromised if no new pseudonyms are available (e.g., the initial supply is exhausted) and it’s not necessary to over-provision a vehicle with pseudonyms. Self-generated pseudonyms must be certified: in the baseline scheme presented below the certification is provided by means of digital certificates created by the CA and installed on the vehicle. We introduce the use of Group Signatures as a mean to self-certify the self-generated pseudonyms while achieving the same level of trust.
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Self-generation of pseudonyms imposes higher transmission and processing costs, due to the use of Group Signatures. Our second contribution consists in a number of optimizations to significantly reduce the overhead without compromising security, that is, by keeping signing and verifying each message. Finally, we identify the problem of changing pseudonyms during an emergency situation, and solve it by proposing an additional scheme. Our goal in this chapter is to analyze the effectiveness of pseudonymous authentication, and how it could be used in practice. We do not aim at formal definitions of privacy, nor to demonstrate that the proposed schemes are provably secure. We refer the reader to the “Background” section for related work in the field with in-depth security analysis.
SCHEME OUTLINE We describe here our proposal for pseudonymous authentication and compare it with pre-existing schemes, the first of which is termed Baseline Pseudonym (BP) and also relies on pseudonyms, while the Group Signatures (GS) relies on a different type of cryptosystem. Our scheme, named Hybrid, essentially combines the two and allows the on-the-fly generation of own pseudonyms with self-certification through the use of bilinear pairings. Baseline Pseudonyms. Each vehicle V is equipped with a set of anonymous public keys, termed pseudonyms, that is, keys certified by the CA and not holding any information about V itself. The CA generates a certificate CertCA(KiV ) on the pseudonym KiV , i-th pseudonym for node V, which uses the corresponding private key kiV to digitally sign its messages and attaches KiV and CertCA(KiV ) to them to allow their verification by other vehicles. The message format is as follows:
Efficient and Reliable Pseudonymous Authentication
m, σiV(m), KiV, CertCA(KiV )
(1)
with σiV () denoting V ‘s signature generated with its i-th private key kiV and m the message payload. To identify the sender from Msg. (1) when needed, the CA needs to maintain a mapping between the set of pseudonyms { KiV } provided to V and V’s long-term identity. Assuming the public key of the CA available, a node receiving Msg. (1) first validates CertCA(KiV ), then if the validation is successful it verifies σiV (m). The validation of CertCA(KiV ) requires to check if KiV is not revoked, and this check is performed through a Certificate Revocation List (CRL) distributed to vehicles via the fixed infrastructure (Raya, Papadimitratos & Hubaux, 2006). This scheme meets all the above security requirements: messages are authenticated and their integrity protected, and digital signatures ensure non-repudiation. As explained above, the CA can perform an inverse mapping and disclose V’s identity when needed. A global eavesdropper can follow V for the lifetime of a given pseudonym, but cannot link the set of pseudonyms used by V to each other, while an active adversary cannot forge m as it does not have access to the needed credentials. This scheme however requires periodic pseudonym refilling, which essentially is a burden for the driver, and an index of poor system design: the driver should not care about the details of vehicular communications security. Group Signatures. This cryptosystem relies on the concept of group, whose members are all the legitimate vehicles registered with the group manager (whose role is played by the CA), which installs a secret group signing key gskV and a public group public key gpkCA. The key property of the cryptosystem is that any member V can issue a group signature ΣCA,V on a message on behalf of the group, and any receiver with the gpkCA can verify that a legitimate group member signed the message but cannot tell which one. Another property is that it’s impossible to link any two
signatures from a group member. The message format is as follows: m, ΣCA,V (m)
(2)
The concept of group signatures was originally introduced by Chaum and van Heyst (1991) and revisited in subsequent works, e.g., (Ateniese & Tsudik, 1999; Brickell et al. 2004; CVIS; Syverson & Stubblebine, 1999). See (Bellare, Miccinaccio & Warinschi, 2003; Bellare, Shi & Zhang, 2004) for formal definitions and security analysis. For the rest of the discussion, we assume and utilize the group signature scheme proposed by Boneh & Shacham (2004), including the revocation method they propose, which is again based on the concept of Revocation List (RL). To allow liability attribution, the scheme allows for an Open operation (Bellare, Miccinaccio & Warinschi, 2003; Bellare, Shi & Zhang, 2004), that the CA only can perform in order to disclose the signer’s identity if presented with Msg. (2) A node receiving Msg. (2) verifies ΣCA,V (m), which tells that a legitimate member generated m. The node itself cannot determine the identity of the node. Being the group signature a digital signature, the integrity of m is also protected and non repudiation ensured. This scheme meets all the above requirements, and guarantees full anonymity as well, at the cost of high channel and processing overhead. The scheme cannot “scale down” the provided anonymity level: nodes are always fully anonymous. Hybrid Scheme. The scheme we propose here is a combination of the two previous ones. Each node is equipped with group keys as in the GS scheme, and generates a set of pseudonyms { KiV } and corresponding private keys. It then generates a group signature ΣCA,V () on each pseudonym KiV, which essentially is a self-certification CertH (KiV) of the pseudonym, with the superscript H indicating that it’s a different type of certificate if compared to the one in BP. V signs the message
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m with the self-generated private key kiV, and then attaches the corresponding pseudonym and selfcertificate CertH (KiV ) as shown below: m, σiV (m), KiV, CertH (KiV )
(3)
Any node with the gpkCA can verify the CertH (KiV ). This validates the pseudonym KiV, which enables verification of the σiV (m). The properties of group signatures guarantee that the receiver can only verify that a legitimate group member signed Msg. (3), but can neither identify V nor can link this message to any previous other CertH (KiV ) sent by V. Messages signed under the same kiV can be linked, but as in the Baseline scheme this can be done only during the lifetime of the pseudonym in use. The σiV (m) provides message integrity, authentication and non-repudiation. As in GS, the Open operation on the CertH (KiV ) allows to disclose the identity of the signer.
OPTIMIZATIONS Optimization 1 The CertH (KiV ) is computed only once at the sender side, as it’s a digital signature computed on a piece of information (the KiV) that doesn’t change (during its lifetime τ). For the same reason it’s validated only once at the receiver’s side and then stored, even if it’s sent in principle with every message. As per the group signature properties, two signatures computed on the same information would look different, but both of them will be accepted as valid at the receiver. Since the KiV is sent in the clear, it’s sufficient to use one single valid CertH (KiV ). This optimization is effective because τ >> ρ-1.
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Optimization 2 The signature σiV (m) is appended to every message, but the pseudonym and the certificate only to every α messages. We term the messages like Msg. (3) as LONG, and the remaining α – 1, carrying only the σiV (m), as SHORT, and as Certificate period. To bind a SHORT message to the corresponding pseudonym, a 4-byte keyID field (a random number) is added to all messages. This Optimization is designed to reduce the packet overhead. However it can harm protocol robustness, if the message carrying Ki+1V, CertHCA(Ki+1V ) is not received. Then, nodes in the range of V must wait for α messages before the next LONG message, while being unable to validate any message from V. The danger arises especially if the vehicles are close to each other and/or move at high relative speeds. Thus, we propose the following scheme to mitigate this problem:
Optimization 3 V repeats the transmission of the new Ki+1V, CertHCA(Ki+1V ) when the pseudonym is changed, for β times. β is named the Push period.
CRYPTOGRAPHIC OVERHEAD In the following of the chapter we’ll use ECDSA (IEEE 1363a, 2004) as signature algorithm for σiV (m), and the Group Signatures algorithm as proposed by Boneh and Shacham (2004), with security levels of t=80 bits for σiV (m) and t=128 for CertH(KiV ) and CertCA(KiV ). In fact, high security is needed only for the long-lived GS keys. A comparison of the chosen algorithms is made in Table 1, with figures for GS derived from an estimate of the needed number of scalar multiplications (Brown et al. (2001); Koblitz & Menezes, 2005) and a benchmark of the same operation
Efficient and Reliable Pseudonymous Authentication
Table 1. Computation costs and communication overhead for different signing algorithms Algorithm
Security level (bits)
Signature (bytes)
Sign (ms)
Verify (ms)
GS
80
151
17.8
15.6
GS
128
225
53.7
49.3
ECDSA192
80
48
0.5
3
ECDSA256
128
64
0.8
4.2
on a 1.5GHz Centrino PC, on which we ran the OpenSSL speed benchmarks for ECDSA.
52.3 ms/msg for verification. Table 2 summarizes the costs per message.
Computation
Overhead Without considering Optimization 2, the security overhead sums to 137 bytes for BP, given by one KiV, one CertCA(KiV ) and one σiV (m). In GS the overhead is given by one ΣCA,V (m), that is, 225 bytes per message. For Hybrid, the overhead is σiV (m), KiV, CertHCA(KiV ), in total 298 bytes per message. The keyID field adds 4 bytes to these figures. For the α - 1 SHORT messages the overhead is the sum of one σiV (m) plus the keyID, that is, 52 bytes.
Table 1 compares the performances of the different algorithms in terms of computation time, signature and key sizes. To compute the processing delays for individual messages it suffices to sum the individual delays of the corresponding primitives. In the BP scheme V computes one ECDSA signature per message, and the receiver validates one ECDSA192 (the σiV (m)) per message and one ECDSA-256 (the CertCA(KiV )) for each KiV. The overall costs are thus 0.5 ms/msg for signing, and 7.2 ms/msg for verification. In the GS scheme the costs are given by one signature/verification of ΣCA,V (m), thus 53.7 ms/ msg for signing and 49.3 ms/msg for verification. In the Hybrid scheme the costs are given by the generation and verification of one σiV (m) plus one CertH (KiV ), totaling 54.2 ms/msg for signing, and
Table 2. Processing costs and overhead for different packet types. © 2008 IEEE, used with permission Sign (ms)
Verify (ms)
Overhead (bytes)
BP LONG
1.3
7.2
141
Hybrid LONG
54.2
52.3
302
SHORT
0.5
3
48
Revocation Revocation is an important side of any public key cryptosystem. Traditional approaches are based either on the offline verification of the public key against a revocation list (RL) or the online verification of the key status (OCSP). In VANET, the second approach requires constant connection to a central server and is thus not feasible in practice, while a revocation list can be periodically disseminated through the RSU. We assume such an approach in the rest of the chapter. We note that the size of the CRL is much higher in BP than in the other approaches, as it includes revoked pseudonyms, not nodes. For BP, each KiV must be checked against a CRL through a number of string comparisons linear to the size of the CRL. For GS, the revocation is performed
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for each ΣCA,V (m) and involves two pairing calculations plus one string comparison, with the RL linear in the number of revoked group members. In the Hybrid scheme the revocation requires two pairing calculations (as in GS), per KiV (as in BP); the RL is again linear in the number of revoked group members. Assuming that a string comparison takes 1e-6 s. and a pairing calculation 5e-3 s., if RLCheck is the cost of comparing one entry in the RL for GS, for BP the same operation costs 1e-4* RLCheck. The cost in Hybrid is again RLCheck as the revocation check in the scheme requires pairing computation. As said before, the CRL for BP includes all the not yet used pseudonyms, with a cost in the order of 1e5 per node. The performances depend on the data structure and search algorithm used to handle the revocation data in memory. Assuming a hash map, the cost is an O(1) search time, with a prior hash map construction needed each time a new RL is received, bearing a O(n) cost. The cost would then be 1e-4 * RLCheck. In the GS scheme, the revocation will take τ * ρ * RLCheck (e.g. for the assumed values of τ = 60 s. and ρ = 10 msg/s. the expression becomes 600 * RLCheck). The comparison is made by considering the total number of signatures that have to be verified against the RL during one pseudonym lifetime: each message requires such verification in GS, while it suffices to do it once per pseudonym in BP and Hybrid.
OPTIMIZATION EVLUATION Simulation Environment Our simulator is partly custom-built in C and partly relies on ns-2, and implements the Dedicated Short Range Communications (DSRC) data link technology (DSRC, 2006). Vehicles transmit beacons, which include vehicle’s direction and location, on a common channel at a rate ρ = 10 beacons/sec. The beacon’s payload is set to 200
256
bytes. Table 2 provides the cryptographic communication overhead per message. We implement the physical layer and a realistic radio propagation model (Nakagami, 1960), as in Torrent-Moreno, Jiang & Hartenstein (2004) and Chen et al., (2007) with an intended (nominal) transmission range of d = 200 meters (DSRC, 2006; Xu et al., 2004) We simulate scenarios of four- and eight-lane highways, with vehicles moving in two opposing two- and four-lane flows. Vehicles are randomly placed along each lane with an average inter-vehicle spacing of s meters, and a random-drawn v. We set s = 20 meters and v = 80km/h. We model wet road conditions by setting the friction coefficient μk to 0.41, which corresponds to vehicle braking capabilities of 4 m/s2. We consider an “emergency braking alarm” application, with such messages transmitted along with beacons, and we investigate how many vehicle collisions occur when safety messaging is used with and without security.
Transportation Safety Simulation Stup We focus on a platoon of one hundred cars in the highway scenario described above. Vehicles in the platoon, denoted as V1 to V100, are placed along a single lane, with V1 at the head and V100 at the back. At a given point in time the leading vehicle V1 performs an emergency brake and starts sending warning messages. Once some Vi, with i > 1 receives the warning, it warns its driver and starts sending warnings itself. We recall that Vj, with j ≠ i, can accept the warning as valid if and only if it already validated Vi’s pseudonym. As proposed by Xu et al., (2004), Yang et al. (2004), Biswas et al. (2006), ElBatt et al. (2006) when Vi receives a warning from a Vj with j > i (e.g. a vehicle behind itself), it stops transmitting warnings. The rationale is that at least one vehicle Vj behind Vi has already been warned, thus it’s in a better position to keep warning Vk, for k > j. We consider here all braking actions as “emergency”.
Efficient and Reliable Pseudonymous Authentication
Braking has two effects: (i) it turns on the vehicle rear red lights that visually warn vehicles within line of sight (as we assume a platoon on a single lane, Vi will only warn Vi+1 with its rear red lights), and (ii) it triggers the transmission of own warning messages. Besides warning other vehicles, a warned Vi clearly warns its driver who starts braking according to how fast are his reflexes. Driver reactions, modeled as a random reaction delay between 0.75 and 1.5 seconds, are triggered by both VC-enabled and visual (red light) warnings. We choose the pseudonym lifetime τ to be 60 s., and we consider the first 60 s. of the simulated time as a warm-up period, during which no emergency conditions arise. By the end of the warm up period, this allows for one pseudonym change and discovery of neighbors by validating their corresponding certificates. This is a realistic situation: at any given point in time, when an emergency arises, vehicles have already discovered some of their neighbors and can immediately validate their warnings. The simulation concludes when all ve-
hicles in the platoon are immobile, as V1 does not resume any motion after its emergency braking, and any Vi cannot physically go beyond Vi-1. These simulated conditions are particularly challenging, with high vehicle density and high average velocities for the given vehicle spacing. High density results in high network load, given the assumed beaconing rates, and thus increased packet loss rates. We do not consider any other optimization to reduce processing overhead beyond considering messages from vehicles with the same heading. This is reasonable for shoulder-separated highways, but we acknowledge that it’s hard to achieve in an urban or rural setting. Finally, we remark that we do not simulate lane-changing as a means to avoid collisions, but leave it as an item for future work. Our results are shown in Figures 1 and 2 respectively: the y axis is the probability of successful reception of a packet, as a function of the distance between the packet sender and receiver (the x-axis).
Figure 1. Probability of successful packet reception, Highway scenario, Baseline scheme, 8 lanes. © 2008 IEEE, used with permission
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Figure 2. Probability of successful packet reception, Highway scenario, Hybrid scheme, 8 lanes. © 2008 IEEE, used with permission
Although the message processing load may be reduced, e.g., by using the node mobility dynamics, this is not the case for the channel load, because any transmission “contributes” in generating interference and increases the number of packet collisions. This is why the probability of message reception is overall lower in Figure 2 than in Figure 1. Looking at each plot individually, safety messaging with “No Security” achieves the highest reliability consistently as the distance changes, while the setting α = 1 achieves the lowest reliability in both schemes, due to
Table 3. Average packet overhead, in bytes per packet, given different security schemes and different α settings. © 2008 IEEE, used with permission α
1
5
10
15
30
50
Hybrid
502
299
273
265
257
253
BP
341
266
257
254
251
250
258
transmission overhead: “No Security” employs a constant packet size of 200 bytes, while each of the different α settings leads to a different perpacket average overhead, as Table 3 shows. Optimization 2 improves communication reliability as α increases (and the corresponding overhead is reduced), and the effect is more evident for Hybrid, which employs larger certificates. Table 3 shows the average communication overhead due to cryptography as a function of α. for both the Hybrid and BP schemes. We observe that “over-optimizing” does not significantly improve channel reliability.
Transportation Safety We compare the impact of safety messaging on transportation safety when used without (the “No Security” plot) and with security protocols. In the latter case we set two values of Optimization 3, β=0 (no use of such optimization) and β=5. With the underlying assumption that nodes are able to process all messages they receive, Figures 3, 4
Efficient and Reliable Pseudonymous Authentication
Figure 3. Collisions in a platoon of 100 cars, 8-lane highway scenario, baseline pseudonym scheme. © 2008 IEEE, used with permission
Figure 4. Collisions in a platoon of 100 cars, 8-lane highway scenario, hybrid scheme. © 2008 IEEE, used with permission
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show the percentage of crashes in the platoon as a function of α. We note that safety messaging reduces crashes to around 10% of the platoon vehicles: this is a significant result as in the absence of vehicle communications, for exactly the same scenarios, 80%-100% of the vehicles crash (we performed the simulations but we do not show it to keep the figures readable). Safety messaging with no security achieves the lowest percentage of crashes, confirming the intuition. We use this case as a benchmark; α is not a relevant parameter and curves in Figures 3, 4 are flat with minor variability due to the randomly seeded simulations. The reasons of its efficiency are the lowest network overhead and no restrictions on which alert message can be validated. In contrast, the tuning of the secure VC protocols affects the effectiveness of the safety application. Figures 3 and 4 show that fewer crashes happen as α increases, but then a more accidents for high α values. One reason is that the increase of α reduces the channel load and thus increases the per-packet reception probability; another reason is that when α is high (e.g. 30 or 50), the authentication delay for a receiver missing a LONG message increases: for example, for α=50 the authentication delay after the loss of one LONG message is 5 seconds, and even more if more LONG messages (and not just one) are missed. Optimization 3 is effective in reducing the number of crashes for a given α compared to the non-optimized case (β=0), by significantly reducing authentication delays at the cost of a slightly higher overhead. Figures 3 and 4 show that β=5 yields an improvement exactly for high α values, where the authentication delay is higher. In the end, Optimization 3 brings the effectiveness of the application close to the unsecured protocol and overall, the investigated safety application, if tuned appropriately, is equally effective on top of both Hybrid and BP schemes.
CONCLUSON AND FUTUREWORK In this chapter we propose to manage security credentials in VANET through self-generation and self-certification of pseudonyms. This greatly simplifies the security management and makes a step towards a usable system. We propose to employ group signatures to generate certificates which satisfy the requirements of anonymity and liability attribution, and we show that the computational cost and the overhead are comparable to the baseline approach thanks to the optimizations we propose. We identify a potential problem in the case of a pseudonym change when vehicles are close, and we propose a mechanism that addresses it. Next, we analyze the costs imposed by security on the transportation systems. First we analyze data link performance, obtaining packet reception probability curves for the pseudonym-based security systems, then we analyze the impact of safety messaging and security and privacyenabling technologies on transportation safety, showing that secure communication schemes achieve safety levels comparable to those with no security at all. Overall, we perform a detailed investigation on pseudonym-based authentication, analyzing several system issues and showing how these security mechanisms can be applied in practice. Future research directions are the dynamic adaptation of the security parameters α and β to the traffic conditions, an effective management of certificates, multiple CA and CRL, and a detailed analysis on the computation overhead. Regarding the latter, we performed the initial steps towards this direction in our previous work (Papadimitratos, Calandriello, Hubaux & Lioy, 2008).
RE Aijaz, A., Bochow, B., Dötzer, F., Festag, A., Gerlach, M., Kroh, R., & Leinmüuller, T. (2006).
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Gerlach, M. (2005). VaneSe - an approach to VANET security. In V2VCOM 2005, San Diego, California, USA. Gerlach, M., Festag, A., Leinmüller, T., Goldacker, C., & Harsch, C. (2007). Security architecture for vehicular communication. In 2nd International Workshop on Intelligent Transportation, Hamburg. Golle, P., Greene, D., & Staddon, J. (2004). Detecting and correcting malicious data in VANETs. In VANET ‘04, (pp. 29-37). New York, NY, USA: ACM Press. IEEE 1363a (2004). IEEE standard specifications for public-key cryptography- amendment 1: Additional techniques. IEEE1609.2 (2006). IEEE trial-use standard for wireless access in vehicular environments - security services for applications and management messages. Jakobsson, M., Wang, X., & Wetzel, S. (2004). Stealth attacks in vehicular technologies. In VTC-Fall 2004. Koblitz, N., & Menezes, A. (2005). Pairing-based cryptography at high security levels. Cryptology ePrint Archive, Report 2005/076. Laberteaux, K., & Hu, Y.-C. (2006). Strong VANET security on a budget. In ESCAR 2006, Berlin, Germany. Li, M., Poovendran, R., Sampigethaya, K., & Huang, L. (2005). CARAVAN: Providing location
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networks. In VANET’04, (pp. 10-18), New York, NY, USA. ACM Press. Torrent-Moreno, M., Santi, P., & Hartenstein, H. (2006). Distributed fair transmit power adjustment for vehicular ad hoc networks. In SECON ‘06, (pp. 479-488). Xu, Q., Mak, T., Ko, J., & Sengupta, R. (2004). Vehicle-to-vehicle safety messaging in DSRC. In VANET ‘04, (pp. 19-28), New York, NY, USA. ACM Press. Yang, X., Liu, J., Zhao, F., & Vaidya, N. H. (2004). A vehicle-to-vehicle communication protocol for cooperative collision warning. In First Annual International Conference on Mobile and Ubiqiotous Systems: Networking and Services. El Zarki, M., Mehrotra, S., Tsudik, G., & Venkatasubramanian, N. (2002). Security issues in a future vehicular network. In Proceedings of EuroWireless 2002 Zeng, K. (2006). Pseudonymous pki for ubiquitous computing. In Atzeni A. & Lioy, A. (Ed.) EuroPKI 2006, (pp.207-222), Turin, Italy, Springer-Verlag.
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Chapter XIV
Simulation of VANET Applications Valentin Cristea University Politehnica of Bucharest, Romania Victor Gradinescu University Politehnica of Bucharest, Romania Cristian Gorgorin University Politehnica of Bucharest, Romania Raluca Diaconescu University Politehnica of Bucharest, Romania Liviu Iftode Rutgers University, USA
ABSTtract This chapter systematically presents actual issues regarding the simulation of VANET applications. Some of them refer to challenges in developing VANET simulators. The chapter discusses simulator architectures, models used for representing the communication among vehicles, vehicles mobility features, and simulation tool implementation methods. A critical analysis of the solutions adopted in some well-known actual simulators is also included. Other issues relate to the use of simulation in the evaluation of applications that aim at improving the traffic safety and control. Representative city and highway application scenarios are discussed, and results that can be obtained by simulation, along with ways these results can be exploited by VANET developers and users are highlighted. Future trends in the development of simulators that produce more accurate results and their use for the evaluation of more sophisticated traffic control solutions are also included. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Simulation of VANET Applications
Introduction Vehicle-to-vehicle communication is a very actual and challenging topic. Vehicles equipped with devices capable of short-range wireless connectivity can form a particular mobile ad-hoc network, VANET—Vehicular Ad-hoc NETwork. The existence of such networks opens the way for a wide range of applications. Two of the most important classes of such applications are those related to route planning and traffic safety. Route planning aims to provide drivers with real-time traffic information, which, in the absence of a VANET, would require an expensive infrastructure. By contrast, the VANET approach is scalable and has low maintenance costs. Moreover, short-range wireless communication technologies (such as 802.11) have no associated cost, other than the communication devices. Safety applications involve disseminating urgent information, which is not present in the driver’s field of view, or it is difficult to notice for reasons such as fog or other vehicles obstructing the line of sight. For instance, a lot of accidents occur in foggy conditions because drivers notice too late that some kind of incident has occurred in front of them. Safety at intersections could also be enhanced, since the risk of collisions could be detected in advance and the driver could be warned seconds before what would otherwise be an imminent accident. The evaluation of VANET protocols and applications could be made through real outdoor experiments, which are time-costly and claim for a large number of resources in order to obtain significant results. Instead, simulation is a much cheaper and easier to use method. Obviously, this leads network and application developers to use simulation in order to evaluate different simple or complicated and innovative solutions before implementing them. In turn, this stimulated the interest for the development of simulators that easily integrate the models and respond to the requirements of VANET applications. Simulators have become indispensable tools at least
in the initial phases of the VANET application engineering process. This chapter aims to present the features and trends in VANET applications simulation, to explain the sources of its complexity and to describe actual solutions. The main challenges are presented in the next section, which introduces the three models involved in VANET simulations: the network and traffic models are determined by the currently used technology; the third model corresponds to the application itself. Then, general features of VANET simulators are presented. We describe an architecture that integrates the first two models and allows an easy interfacing with the third model. Details about the network / communication model are also given, and several components of the traffic / mobility model are then discussed. Current challenges in developing VANET simulators, and the description of solutions adopted so far are subjects of a separate section, in which issues regarding the performance of different simulation tools are presented. Another section is dedicated to application scenarios, the results that can be obtained by simulation, and the ways these results can be exploited by VANET developers and users to improve the car-to-car communication and the traffic control. The chapter ends with conclusions and future trends.
Background Simulating a VANET involves two different aspects. First, there are issues related to the communication among vehicles. Network simulators, like The Network Simulator—ns-2 (2008) and Jist/ SWANS (2008) cope with communication issues and focus on network protocol characteristics. The second very important aspect is related to the mobility of the VANET nodes. Traffic simulators take into account the traffic model, not necessarily in conjunction with VANETs. For example, TRANS—Traffic Network Simulator
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(Katz, 1963) was used to optimize the timing of traffic lights in the absence of inter-vehicular communication. In the majority of VANET applications, vehicles react to messages. For instance, if a driver receives a message saying that the road ahead is congested, that driver will probably change her/his route. In order to study such reactions, an integrated simulator is needed, one in which the relationship between the vehicle mobility module and the network communication module is bidirectional. Choffnes and Bustamante (2005) showed that the vehicular mobility (traffic) model is very important, and its integration with the wireless network model could produce more significant results. The authors present an integrated simulator that uses an original vehicular traffic model called Street Random Waypoint (STRAW) implemented on top of JiST/SWANS (2008). The authors have used the simulator to show that studying routing protocols for a vehicular network without an accurate vehicular traffic model is a wrong approach. In this respect, they compared their own results with those obtained with the Random Waypoint model (Broch, 1998), which is a very inaccurate representation of a vehicular network. The mobility model implemented in some simulators is not a sufficiently accurate representation of actual vehicle mobility. For example, in the model of Saha and Johnson (2004), each vehicle moves completely independent of other vehicles, with a constant speed randomly chosen. Multi-lane roads or traffic control systems are not taken into consideration. Other authors (Mangharam et al., 2005) make similar simplifying assumptions and do not consider multi-lane roads or car following models. The mobility model of Choffnes and Bustamante (2005) is more complex: the motion of a vehicle is influenced by the preceding vehicle, and traffic control systems are considered. However, multi-lane roads are not taken into consideration. A more accurate model has been used in the development of an integrated vehicular networks
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simulator, VNSim (Gorgorin, 2006), which includes a complex model for vehicles mobility, a wireless network simulator, and an interface for the emulation of vehicular applications. The simulator can be used to analyze networks of several thousands of nodes in complex city scenarios as well as in highway scenarios. The simulator allows the evaluation of a large range of vehicular computing applications, which cannot be studied by using other simulators, and can be used to improve both car-to-car communication protocols and traffic control applications (Gradinescu, 2007; Diaconescu, 2007).
GENERral FEAatur of VANET Simulator VANET simulators can be classified as microscopic or macroscopic. Microscopic traffic simulators emphasize local behavior of individual vehicles by representing the velocity and position of each vehicle at a given moment. This type of simulation is especially helpful for studying localized traffic interactions, but it comes with the price of reduced scalability. Macroscopic simulators capture traffic conditions in a global manner and may use concepts from wave theory. Usually, the traffic is represented in terms of flows (vehicle/ hour), density (vehicles/km) or average speed. Similarly, microscopic network simulators take into account the transmission of each individual packet (or message), while the macroscopic network simulators consider transmission statistical properties. Most authors agree that a macroscopic mobility model is not sufficient to allow the study of a vehicular network, therefore microscopic simulation, although more complex, is required. The evaluation of vehicular applications for online traffic control (that produce real time feedback to the driver), such as safety applications, also requires a model of driver behavior. Models for lane changing or car-following are
Simulation of VANET Applications
some of the features that a microscopic VANET simulator has to implement to allow the analysis of such applications.
time. The simulation time is quantified in fixed pieces so that each event can be associated to a specific timestamp with an acceptable resolution. At every moment of the simulation time, the Event Engine pulls the current events from the queue, and handles them in a random order by calling the event processing modules. The vehicular traffic component is represented by the Mobility Module, while the wireless network component is incorporated into the Network Module and Scheduler. The Event Queue can hold three types of events: send, receive or GPS. A send event for a specified node triggers the call to the node’s procedure responsible for preparing a message. It also schedules the corresponding receive event(s) for the message receiver(s) that are determined by the simulator according to the wireless network model. The receive event is associated with a node (group of nodes) to which the message is transmitted (broadcasted). Its action is to call the appropriate handler in each of the receiving
Simulator Architecture A VANET simulator has two main components: a network component, capable of simulating the behavior of a wireless network, and a vehicular traffic component, able to provide an accurate mobility model for the nodes of a VANET. Depending on the simulation type, the simulator can contain other components as well. To describe them, we refer to the VNSim presented in (Gorgorin, 2006). Figure 1 shows the general view of VNSim. This is a microscopic discrete-event simulator. Consequently, it includes components for event management: an Event Queue, where the events are registered in the order of their occurrence times, the modules for events processing, and a logical clock for the management of the simulation
Figure 1. General VNSim architecture Vehicle 1 Receive Event Receive Message
GPS Event
Send Handler Prepare message
Vehicle n
Own Data
Event Engine
Send Event Send Message
Vehicle 2
Receive Handler
GPS data Vehicular Mobility Model
Event Queue
Network Module and Scheduler
Mobility Module
New Events
3.2s 4
3.3 s
.0s
VANET SIMULATOR
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nodes. The GPS event is scheduled at a regular time interval, for each node, in order to simulate the way an actual VANET application periodically collects GPS data. The mobility module repeatedly updates the position of each node representing a vehicle, according to the vehicular mobility model. This model takes into account vehicle interactions (passing by, car following patterns, etc.), traffic rules, and the behavior of different drivers. The main advantage of this architecture is the possibility to execute (or emulate) the actual code of a vehicular application without significant changes, by using appropriate simulator interfaces. In other cases, the vehicular mobility and wireless network models can be incorporated in different simulation tools. For example, MOVE—MObility model generator for VEhicular networks (2008) uses SUMO—Simulation of Urban MObility (2008) for simulating a traffic scenario and generating an output file with vehicular mobility traces. This file is offered as an input to a network simulator, such as ns-2 or Qualnet (2008), to obtain the final simulation results. A similar solution is adopted in (Eichler, 2005), where the authors propose the coupling of the CARISMA Traffic simulator with ns-2 and with the application module, in order to study the vehicle-to-vehicle warning-message distribution. Synchronization data is exchanged periodically between the two simulators. The challenge is to ensure the “coupling of simulators”, i.e. the consistency of vehicle positions in both simulators, at synchronization points. The simple architecture mentioned above makes use of existing traffic and network simulators. The drawback is that it does not support the simulation of complex VANET applications in which the driver behavior would be influenced by the application itself, as already discussed before. An innovative architecture aiming at solving this problem is currently under evaluation by the authors of TraNS (Piorkowski, 2008). They are
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trying to develop a general framework for controlling the mobility, without being limited to a specific traffic simulator or to a specific network simulator. This architecture promises to offer new possibilities for simulating VANET applications, but the framework has not yet been released. A slightly different architectural approach is the hybrid simulation (Killat, 2007). The simulator architecture incorporates a previously implemented vehicular traffic simulator VISSIM and two new components: VCOM, used to simulate the inter-vehicle communication on the basis of a statistical model proposed by the authors, and an application module. The simulator has to respond to specific user requirements regarding the functionality (it was aimed to investigate the impact of car-to-x communications on transport efficiency) and the performance (scalability, short response time, and easy of use). The relevant characteristics of a VANET simulator are the simulation efficiency and accuracy for large scale VANETs, which we will address in a separate section.
Communication Models Communication models highlight the information flows between vehicles and are of special interest when vehicles move with high relative speeds. VANET applications are affected by wireless networking aspects such as transmission delay, packet loss or network access scheme. However, accurate network simulation introduces additional complexity and makes several large scale VANET applications unsuitable for simulation. In this section, we discuss the network characteristics that are mostly relevant for VANET simulation, as well as the accuracy that can be reached with their implementation. At the physical layer, the reliability of the wireless network, in terms of packet loss and transmission delays, is relevant to simulation. Several models are used for the radio waves propagation, which consider estimates of the power of a received signal. This signal can be received by an antenna
Simulation of VANET Applications
only if the Signal-To-Noise (SNR) ratio is over a specific SNR Threshold (SNRT). Wave propagation models have been studied extensively for decades in telecommunication and recently have been correlated with vehicular short range communication. The radio signal can be degraded by fading. This is caused by signal propagation over multiple paths (with different values for the attenuation, delay and phase shift), which produces interferences at the receiving antenna. There are several examples of such propagation models that were implemented in wireless simulators. Free Space Model is the simplest one, which considers ideal conditions: a single radio signal that attenuates with distance. Two Ray Ground propagation model considers two rays between the source and the destination: the direct line and the ground reflection. It gives more accurate estimates than the free space model, especially for long distances. VNSim (Gorgorin, 2006) implements this model and includes the signal attenuation with distance in order to test different ways to optimize the network traffic. Rayleigh fading (Sklar, 1997) models multiple indirect paths between the sender and the receiver, while the Ricean fading model considers only one dominant line of sight and multiple indirect signals. Reports from real experiments have shown that vehicular short-range wireless networks can be modeled using the Nakagami fading model (Sklar, 1997), which considers fast fading to characterize the wireless channel. Concerning the protocols, DSRC—Dedicated Short-Range Communications Protocol (Taliwal, 2004) is a multi-channel wireless protocol, which is based on the IEEE 802.11a Physical Layer and the IEEE 802.11 MAC Layer. It operates over a 75 MHz licensed spectrum in the 5.9 GHz band allocated by the Federal Communications Commission, and supports low latency vehicle-to-vehicle and vehicle-to-infrastructure communications. The motivation behind the development of DSRC
is mainly the need for a more tightly controlled spectrum for maximized reliability. The use of hand-held and hands-free devices on the 2.4 and 5 GHz bands along with the increase of WiFi could cause intolerable and uncontrollable levels of interference, which would significantly decrease the reliability and effectiveness of vehicular safety applications. But, even with a licensed band, some issues arise, such as fair access to all applications, including priority scheduling of traffic between different application classes (safety over nonsafety). Unlike 802.11, multi-channel coordination is a fundamental capability of DSRC. Consistent effort has been put into matching simulation results to real outdoor experiments. Yin et al. (2004) observe significant degradation of the network performance under the high node mobility. The study evaluated the DSRC Physical Layer and the MAC Layer, in the context of a traffic safety application, on top of a customized simulator combining the CORSIM (2008) traffic simulator and the Qualnet (2001) network simulator. It showed a long term average throughput of 50-60%. Using the Nakagami m-distribution fading model implemented in ns-2, Killat et al. (2007) estimated the probability of successful reception at a given distance depending on vehicle density and data load. Their idea is to combine discreteevent simulation and mathematical modeling, starting from the observation that the data transmission that is actually interesting and relevant for a VANET application represents only a small percentage of the total data exchanged during a traffic scenario. There is a lot of “background traffic”, which can be much more efficiently simulated with statistical methods, while keeping the more accurate discrete-event simulation techniques only for “interesting” data. Their work is based on the concept of “hybrid simulation” and offers much better performance compared to purely discrete-event approaches. Although actual outdoor experiments have been made, the behavior of the wireless network (the DSRC
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protocol) under stressed city-like scenarios with high node density is still an open issue. This is why simulation frameworks now focus on large scale VANET simulations. A lot of work has been done to study the routing layer protocols in mobile ad-hoc networks, MANETs. They can be grouped in two categories. The first category includes protocols that use the IP addresses to locate nodes, such as: DSDV—Destination-Sequence Distance Vector Routing (Perkin, 1994), DSR—Dynamic Source Routing (Johnson, 2001), AODV—Ad hoc on demand distance vector (rfc3561, 2008), and MPR—Mobile Relay Protocol (Nain, 2004). Protocols in the second category are based on location information. Some examples are: DREAM (Basagni, 1998), GPSR—Greedy Perimeter Stateless Routing (Karp, 2000), and ABRP—Anchor Based Routing Protocol (Huaizhi, 2005). Classical MANET protocols have been proven to perform poorly (Blum, 2004) due to specific characteristics of vehicular ad-hoc networks, namely: rapid topology changes, small network diameter, and limited redundancy. This is why other position-based forwarding solutions have been proposed (Festag, 2004) for vehicular environments, which take into account some elements of the vehicle mobility model such as the distance between communicating partners and the available paths towards destination. This led to a combined position-based and map-based ad hoc routing. Mobility Models Mobility models emphasize local “behavior” of individual vehicles by representing the velocity and position of each vehicle at a given moment. This type of simulation is especially helpful for studying localized traffic interactions. Vehicular traffic mobility models have been studied for a long time. However, until recently, the purpose of developing and studying accurate mobility models has not been to use them for
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VANET simulation. In this section, we review vehicular mobility models, from the perspective of a VANET simulator architect. Generally, when choosing a mobility model for a VANET simulator, the complexity and the accuracy are the most important issues need to be considered. The benefit of using a very complex and accurate mobility model, together with the performance penalty, should be carefully evaluated, keeping in mind the purpose of the VANET simulator and the type of applications for which it is designed. In the Random Waypoint mobility model (Broch 1998), each mobile node has a position and a velocity, and moves towards a random destination in a predictable way that can be calculated as a function of time. This model was used originally in ns-2 (2008). It was designed for general-purpose mobile networks and does not accurately represent vehicular motion. Jardosh et al. (2003) define a more complicated model that corresponds to urban settings, in which obstacles are introduced to constrain vehicles’ mobility and the wireless transmission. Two other models have been defined by Potnis and Mahajan (2006). The Stop Sign Model (SSM) introduces stop signs at every intersection. Every vehicle arriving at the intersection would wait for a fixed period of time before proceeding to its destination. The Traffic Sign Model (TSM) introduces traffic lights at every intersection. A vehicle arriving at an intersection waits for a random period. Both models introduce queues at intersections and interdependencies among vehicles on the same street. A thorough analysis of these models is described in (Mahajan et al., 2007). The authors studied the dependency of the packet delivery ratio and delay on the characteristics of the urban settings such as street layout, traffic rules, etc. In some studies (Baumann et al., 2008), specific models such as the Manhattan model and GIS models, are used to establish the street layouts.
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In the model of Saha and Johnson (2004), each vehicle starts at some randomly selected point on a road and moves until a destination point on another randomly selected road. Several new elements are added to the model and to its use. First, the path between the starting and destination points is calculated by using the Dijkstra’s shortest path algorithm. Second, the vehicle speed is also randomly calculated using a uniform distribution within a specified interval. Third, the authors use the TIGER map database to create a more realistic environment of the simulation study. The mobility model of Choffnes and Bustamante (2005) is more complex: the motion of a vehicle is influenced by the preceding vehicle (car following model), and traffic control systems are considered to introduce some actual traffic conditions such as traffic congestion. In addition, the model includes communication details, such as the percentage of cars actively communicating, and road maps details, such as characteristics of street segments and number of lanes in each direction. The study of safety and traffic control applications requires an accurate model of driver behavior. One example is the model developed by Wiedemann (1974, 1991) and further studied by Fellendorf and Vortisch (2001). This psychophysical model is used in the commercial traffic simulator VISSIM (2008). The basic assumption is that a driver can be in one of four modes, which are classified according to the driver’s convenience: free driving (superior convenience), approaching, following, and braking (lowest convenience). In the free driving mode there is no influence from preceding vehicles on the same lane. In this situation, the driver will seek to obtain and maintain a “desired” speed. The desired speed and the acceleration depend on driver’s personality, and on road’s characteristics. In the approaching mode, there is a slower preceding vehicle that influences the driver. In this situation, the driver decelerates in order to obtain the same speed as the preceding vehicle. The deceleration is a function of the
distance between the two vehicles, their speeds, as well as other parameters. In the following mode, there is a current and a preceding vehicle, but the speeds of the two vehicles are practically equal. In this situation, the driver will seek to keep the speed constant. In the braking mode, there is a slower preceding vehicle, very close in front. In this mode, due to the immediate danger, the driver will apply high deceleration rates. This model was implemented in VNSim, together with a lane-changing model for multilane roads, based on lane-usage rules valid for several European countries. According to these rules, the use of the first lane is required, unless it is occupied. It means that a driver will always try to stay on the lower lanes, except when overtaking another slower vehicle. Overtaking on the right side is not allowed. Whenever a driver is in a different mode than free driving, she/he will always check if the higher lane can provide a superior mode. If this is the case, the driver will switch to a higher lane. Similarly, whenever a driver is in a different mode than braking, she/he will always check if the lower lane provides at least equal conditions. In this case, the driver will switch to a lower lane. Different driver profiles (aggressive, regular, calm) can easily be modeled by using several parameters: distance between vehicles, maximum speed, desired speed, driver reaction time, critical distance for driver reaction, etc. Each driver class is represented by a specific set of values for these parameters. In order to further differentiate the drivers, the model allows small deviations from the specified values, which are randomly computed for each individual driver. Fellendorf and Vortisch (2001) showed that the model is accurate, by comparing simulated traces with actual measurement data taken from a German freeway and from a US freeway. To further calibrate the model, the authors of VNSim performed an extra validation, by measuring real-time traffic in intersections and comparing the results to those obtained by simulation (Gradinescu, 2007).
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Estimating fuel consumption and pollutant emissions is an increasingly important issue when studying vehicular traffic. The model of Akcelik and Besley (2003) considers the estimation of the relation between fuel consumption and emissions, on one side, and the speed and acceleration of the vehicle, on the other. A simplified version of this was used in VNSim to take into account only light vehicles. Statistics and global measures have been easily obtained.
Maps A digital map is required in almost any VANET application. Each vehicle that is part of the system should have access to such a digital map. A very popular option for digital maps used by VANET simulators is TIGER—Topologically Integrated Geographic Encoding and Referencing (2008), which is freely-available, actual digital maps for the USA. Most of the VANET simulators use TIGER files as the source for their digital maps. TIGER files contain detailed geographical information about the roads in a region, from large highways to small streets. For each road, the TIGER files specify its end points, along with as many intermediate points as needed, depending on the road’s shape. Furthermore, for each road, a “class” information is given (whether it is a small street, a local road, a State Route, or an Interstate Highway). Unfortunately, the TIGER database lacks more detailed traffic-specific information, like the number of lanes, or traffic control systems, like traffic lights, yield or stop signs. In more realistic simulations, such information is required. The authors of VNSim have chosen to add the extra information, based on simple heuristics and on the road class information included in the TIGER files. For example, more lanes are considered for higher class roads, yield or stop signs are introduced for lower class roads, traffic lights are used at intersections between equally important roads, etc.
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Implntation i Approaches to Simulator Development The development of VANET simulators follows two different approaches. One is to implement the mobility model from scratch. In this situation, the integration between the mobility model and the network communication model is ensured in the design phase. The integration is implicitly achieved since the developers start with this integration in mind. On the other hand, the complexity of realistic models for communication and mobility makes this approach a very challenging one. There is a huge amount of work required to implement complex models. A typical example of this approach is GrooveSim (Mangharam, 2005), which is a simulator for geographic routing in VANETs. It has several modes of operation that allow the evaluation of network protocols, extraction of mobile wireless propagation models in real time, the generation of test scenarios for simulation, hybrid simulation with real and virtual vehicles, and others. The most important limitation of GrooveSim is the simplistic mobility model, which does not consider car-following or lane-changing models. The authors acknowledge this limitation and mention that the focus of their work is to provide basic models and evaluate their impact on key performance metrics. STRAW—STreet RAndom Waypoint (Choffnes, 2005) builds a mobility model that constrains vehicles’ movement according to the map of a city, different traffic profiles (for example vehicular congestion) and specific control mechanisms. It is implemented as an extension to a wireless network simulator developed by Barr (2004) at Cornell University. The mobility model of STRAW is also simplistic, and does not consider multi-lane roads. Saha and Johnson (2004) based their simulation on building a realistic mobility model com-
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patible with the ns-2 network simulator. Their development makes use of the TIGER database. The mobility model which they implement is very simple, without considering car-following or lane-changing models. However, their simulations are among the first ones which prove that using a different mobility model than the wellknown random waypoint model will generate significantly different results, thus proving that the mobility model is an important component of a VANET simulator The other approach is to use an existing mobility model implementation and integrate it with a network simulator. This approach takes advantage from existing vehicular traffic simulators, which already implement complex, validated mobility models. The challenge of this approach is to achieve an online integration, in which the movement of vehicles is directly influenced by the VANET applications running on some of the vehicles. The easier way for integration is to generate traffic patterns offline, and then use these results as an input to a network simulator. VanetMobiSim (Harri et al., 2006) is a generator of realistic vehicular movement traces for network simulators. It considers macro-mobility features, such as road topology, number of lanes, speed limits, etc., and micro-mobility characteristics, like crossroads regulated by traffic signs or lane change capability for vehicles. Similarly, the Generic Mobility Simulation Framework—GMSF (Baumann et al., 2008), which models and simulates node mobility, is designed to work together with a network simulator. GMSF has some important distinctive features: it implements new GIS-based models with detailed road maps, and includes microscopic features such as traffic lights management. An example of using mobility traces is given in (Yang et al., 2007). The authors propose an evaluation framework for VANET applications. It utilizes a hybrid emulation testbed TWINE, which combines real implementation of high level network layers (closer to the application) with the
simulation of low level layers (physical, MAC). The framework offers several advantages to its users: it allows the execution of real applications in a simulated network environment; it integrates realistic mobility traces and deployment data; it uses evaluation metrics related to the application. A similar approach is described in (Rehunathan et al., 2007), where a microscopic traffic simulator, MITSIMLab is used to generate vehicle movement traces to be used in ns-2. This approach, however, does not allow for an accurate evaluation of a VANET application or protocol, because the mobility pattern cannot be modified as a reaction to the VANET application. Using an existing traffic simulator, which is based on already validated complex traffic models, has the important advantage of avoiding “re-inventing the wheel”. Among the most recent attempts to implement a VANET simulator by using this approach are MOVE (Karnadi et al., 2007) and TraNS—Traffic and Network Simulator (Piorkowski et al., 2008). Both use the open-source microscopic traffic simulator SUMO (2008), which implements complex validated vehicular traffic mobility models. And both use two steps for the VANET simulation. In the first step, traffic traces are generated offline using the traffic simulator. In the second step, a network simulator processes the traffic traces and evaluates the network communication between the nodes. The authors of both simulators agree that the approach does not allow the simulation of complex VANET applications, in which the driver behavior would be changed by the application itself. However, both developer teams mention that this is a goal for future work that will achieve a real integration between the traffic mobility model and the network communication model. Furthermore, the authors of TraNS claim that their simulator is “the first open source project that attempts to realize this highly pursued coupling for application-centric VANET evaluation” (Piorkowski et al., 2008).
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Sommer et al. (2008) describe the simulation framework Veins (Vehicles in Network Simulation) that is based on the bidirectional coupling of OMNeT++, a network simulator capable of modeling realistic communication patterns of VANET nodes, and of SUMO, the microscopic traffic simulator. The bidirectional coupling is able to support the analysis of more realistic traffic models that reflect the impact of inter vehicle communication on the road traffic.
Accurate simulation of vehicles’ mobility and wireless transmission is time-costly. Also, for an application designer, the study of many application scenarios requires experiments with large maps and with large numbers of vehicles. This is why the scalability of VANET simulators is a main performance metric for which we present some guiding figures. Experiments performed with VNSim on a 1.6GHz uni-processor showed that it can support around 10.000 network events per second. This value is lower than the throughput of the widelyused network simulator ns-2, which can simulate over 60.000 events per second, on a 2GHz uniprocessor. This is explained by the of the network
simulator with a complex node mobility model, which is responsible for accurately computing the motion of all nodes, in each time quantum. The graph in Figure 2 shows the evolution of the simulator’s performance with the increase of the number of nodes. The results are based on simulations performed in a highway traffic scenario, with all the nodes running a neighbordiscovery and update protocol, with a 1 second period for the beacon messages. VNSim is able to simulate motion of 1000 vehicles, in a complex city-scenario in real-time (1 second of simulation in 1 second of real time). Experiments have been done by the authors with a squared region of 1km by 1km in Manhattan downtown, which has a high number of intersections and traffic lights (see Figure 3). VNSim was also used to perform the simulation of more complex scenarios, involving up to 10.000 vehicles. NCTuns (Wang, 2008), a highly-integrated simulator, cannot be used for large-scale simulations. Thus, for the simulation of a scenario with 100 vehicles, 22 minutes are required. For the same scenario, if the number of vehicles doubles then the amount of time required for the simulation grows approximately 4 times, to 84 minutes. These results, obtained on a 1.8 GHz uniprocessor, show that the simulation of large-scale vehicular
Figure 2 VNSim performance evolution with the increase of the number of nodes
Figure 3 Squared region in Manhattan downtown
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networks (with tens of thousands of vehicles) can be achieved. Some performance results are presented in (Choffnes, 2005) for the STRAW simulator. Here, STRAW’s overhead has been evaluated for two models: a simple one in which vehicle movement is determined randomly at each intersection, and a more complicated one in which the movements are determined according to pre-computed shortest paths. The evaluation was done by disabling the wireless communication simulator. The experiments were performed on a Pentium 4 at 2.4GHz computer with HyperThreading enabled. When the number of nodes considered in the experiment increased from 100 to 1600, the runtime increased four times for the simple model and 6.5 times for the more complicated model. The highest overhead of 10% of the simulation time has been registered for the complex model with 1600 vehicles. One solution to improve simulators’ performance is parallelization. Fujimoto et al (2003) report experiments performed on packet level network simulators. Parallelization is achieved by federating several copies of the same simulator or of different simulators. This approach was used to produce two parallel simulators, PDNS—Parallel / Distributed Network Simulator (PDNS) based on ns-2 and GTNetS developed at Georgia Tech. The results showed a very good performance measured in number of transmissions per second, PTS—Packet Transmissions that can be simulated per Second. More specific, for the best case of parallel simulations, the number of PTS was 50 times higher than the number of PTS obtained in the best sequential case. MoVES, a Mobile Wireless Vehicular Environment Simulator (Bononi et al., 2006) is also parallel and distributed. Its idea is to distribute subsets of the model entities to different processors that communicate via message passing. The authors find that the parallel approach gives
good results when simulating massive wireless vehicular networks. Higher performance can also be achieved by the use of hybrid simulators (Killat, 2007). The authors present the results obtained for 2.500 to 3.000 vehicles in a scenario dedicated to the analysis of the impact of the penetration rate of radio-equipped vehicles on the average speed. The speedup factor was 500 as compared to the NS-2 simulation. The result is due to the use of the hybrid simulation concept, which combines the discrete-event simulation with analytical traffic modeling. As a general conclusion, developing a highlyintegrated simulator, with complex models for both the vehicular mobility and the wireless communication has the advantage of higher accuracy but has a severe impact on performance. Integrating existing vehicular traffic simulators and network simulators can lead to better performance but it is not able to support more complex scenarios involved in safety and traffic control applications. Improvements can be achieved by parallelization, which distributes the simulation task to different computing resources, or by selecting a model that reduces the computation load.
Simulation of rntati city and highway application SCENnario This section presents several simulation use cases together with results obtained in realistic simulation experiments performed with the VNSim simulator. The way these results could be exploited by VANET developers and users are also highlighted. An important class of applications that can be deployed over VANETs is concerned with traffic operations and maintenance: dynamic route planning, weather conditions publishing and adaptive signal control in intersections.
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Traffic coordination in intersections is a very challenging and not enough studied topic. As an example of a VANET application, we present an adaptive traffic light system based on wireless communication between vehicles and fixed controller nodes deployed in intersections (Gradinescu et al., 2007). We describe the steps required to deploy such an application in VNSim. Furthermore, we present the results showing significant improve of traffic fluency in intersections, and the advantages over other architectures in terms of cost and performance. The goal of the VNSim project was to reduce congestion and the number of accidents in intersections by enabling wireless communication between traffic lights and vehicles. In this way, the traffic lights can get a real-time “image” of the traffic, and make an informed decision on how to set signal plans. As a secondary benefit, the traffic lights can also detect dangerous situations and alert drivers about the intersection status to increase their awareness. The following aspects need to be taken into consideration when such an application is simulated. First, there must be a data dissemination system that supports the information flow from vehicle to vehicle and to the traffic light. Based on the TrafficView system (Dashtinezhad et. al 2004), data dissemination was designed so that vehicles forward information about other vehicles they know about (position, speed, direction) to all the other vehicles in their range.
The simulation of this propagation scheme in VNSim demonstrated that a lot of redundant data is transmitted and a high data charge is put on the network. Moreover, for large packet size, the probability of the successful transmissions decreases due to wireless interferences. A solution is the use of aggregation mechanisms. A possible method is the use of a probabilistic scheme in which only some “leader” vehicle, chosen randomly with a probability depending on the traffic density, transmit the information. The simulation experiments highlighted the great decrease in the network load while preserving the functionality of the solution. The data dissemination algorithm supports the implementation of the traffic light control that establishes a signaling plan well suited for the intersection. Considering the traffic light is a node participating in the dissemination process, it can gather traffic metric values (flows, delays) on all the concurrent roads. Optimum cycle length and green split phases are computed using traffic engineering methods, such as Webster’s formula (Gradinescu et. al 2007). One of the simulation scenarios that were used to analyze the efficiency of the algorithm was a 4-way intersection in Bucharest during rush hour. As shown in Figure 5, the intersection recovered sooner from congestion when using the adaptive strategy described above, compared to the pre-timed system deployed in the intersection. The total delay measured for all vehicles was decreased by 28%.
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An additional component of the VNSim simulator is the module for estimating the pollutant emissions and fuel consumption, which considers the particular behavior of each vehicle speed and acceleration. This feature was used in this application to demonstrate the benefits of the adaptive solution due to increased traffic fluency: 6.5 % improvements in fuel consumption and CO2 emissions were measured.
Collaborative Route Computation Finding the best route to destination in congested roads traffic is a major challenge in the attempt to improve traffic conditions. Collaborative route planning is the second VANET application we use to show how ad-hoc communications can improve the driving experience. The simulation of a collaborative, dynamic routing application requires several features from the simulation tool. First, in order to study a congested roads network, a large number of vehicles, moving on a large map, should be supported. Therefore, the simulator’s performance and scalability are crucial. Furthermore, this kind of application supposes that the mobility of the vehicles is influenced by the application itself: if drivers find out, by using the VANET route planning application, that a road is less congested, then they might change their initial path in order to take advantage of this information. A simulator which does not integrate the vehicular mobility module with the application module will not be able to represent dynamic route changes. Diaconescu et al. (2007) used VNSim in order to study the dynamic route computation problem. The authors discuss two different approaches. The first is to exclusively use the ad-hoc network for all the information exchanges. The second approach is based on the existence of fixed nodes, placed in intersections, which also participate to, and facilitate the communication. In the first approach, each driver first computes a route towards its destination. This initial
computation is based on distance, on the roads’ types, and on the assumption that drivers tend to prefer the main roads. After this initial computation, the driver starts the journey. Along the way, periodically, queries about the traffic conditions ahead are sent via the ad-hoc network. Depending on the replies (for example, the reception of information about traffic jams ahead), re-calculations of the route occur. The simulation experiments showed that global improvements in travel times occur when only a percentage of the vehicles use the algorithm, not all of them. When all vehicles change their route, the traffic jam “moves” to another road. Starting from this observation, the authors suggested the use of fixed-nodes placed in intersections, which are able to communicate with vehicles and, at the same time, are inter-connected through a separate highspeed network. Of course, this solution requires a more extensive infrastructure. In large cities, a large part of this infrastructure might already be present. The fixed-nodes gather information about the actual traffic (number of vehicles traveling) on each road and execute a collaborative route planning with the other fixed nodes. The results are sent to the vehicles in proximity informing them about the best routes.
Highway Lane Reservation An innovative VANET application was proposed in (Ravi et al., 2007). The authors argue that time guarantees of other means of transportation (like train or plane) can be incorporated into the highway system. The idea is to allow drivers to reserve an “entry slot” onto dedicated lanes of a highway by paying a premium price. Reservations will be allowed for these lanes up to their carrying capacity, so that the dedicated lanes will never become jammed and the system can guarantee the trip duration between any two highway points, in the absence of accidents. The general architecture of the system is shown in Figure 6. The proposed system is complex and is
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Figure 6. Lane reservation general architecture
Concluion
made up of several subsystems. First, a reservation system is required to allow users make advance reservations of time-slots on the dedicated lanes. Then, an enforcement system is needed to ensure that only drivers with valid reservations travel on the dedicated lanes. This could be achieved by using RFID tags located on vehicles, and RFID readers placed on the highway. The lane entrance must be assisted by a VANET. More specific, since the dedicated lane will often be used at near-to-maximum capacity, it is important that vehicles that enter or leave the lane do not create disruptions in the traffic flow on this lane. In order to allow smooth entering and leaving, a VANET protocol can be designed. For example, the car that wants to enter the lane will send a request to a vehicle already on the lane, asking for permission to enter. In order to evaluate such a large-scale system, a proper simulation tool is needed. It should be scalable enough to be able to simulate a large-scale highway traffic scenario. Furthermore, emulating the application code run by each node would also be required. The authors plan to investigate the possibility of using VNSim in order to study and validate a lane reservation system.
This chapter discussed the simulation of VANET applications. The focus was on the new trends in communication protocols and traffic models, and on new facilities incorporated in simulation tools. The advances in the VANET technology and protocols support the adoption and use of more complex mobility models and of more flexible and adaptable traffic controls. VANETs have specific characteristics such as the rapid topology changes or the changes in the vehicles mobility as reaction to traffic changes. Obviously, these changes must be captured by the simulation models, which become more or less complicated and include more elements that constrain the vehicle mobility: maps, real traffic conditions (congestion), driver behavior, fuel consumption, pollutant emissions, etc. Recent research referenced in this chapter targeted the validation of these models and the exploitation of their facilities in different applications. Realistic models, such as those used in lane reservation, were proposed and will eventually constitute the subject of further research. In order to more accurately evaluate the traffic control applications, more powerful microscopic simulator tools have been developed. The tools integrate the network communication model and the traffic / mobility model, and facilitate the interfacing with the core application modules. One drawback is the high time consumption required by simulation experiments besides other aspects like usability and flexibility. Since microscopic simulators demonstrated their utility in the VANET applications design process, the increase of tool performance, scalability, and flexibility will be subjects to future research work. The technological progress will stimulate new applications aiming to improve the traffic safety and control strategies. More vehicles will be equipped with GPS receivers, communication devices compatible with different protocols (for example DSRC), radars for identifying neighbors’ positions, equipment for storing digital maps,
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etc. These and other similar components will facilitate the development of more complex safety applications (like imminent collision warning, obstacle detection, collision avoidance, etc.) and traffic control applications (like traffic congestion avoidance, traffic lights control, route computation, etc.). Obviously, the role of simulation in the evaluation of different solutions will become even more important.
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Gorgorin, C., Gradinescu, V., Diaconescu, R., Cristea, V. & Iftode, L. (2006). An Integrated Vehicular and Network Simulator for Vehicular Ad-hoc Networks. In Proceedings of the Eurosis European Simulation and Modeling Conference 2006 (pp. 142-149). Toulouse, France. Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V., & Iftode, L. (2007). Adaptive Traffic Lights Using Car-to-Car Communication. In Proceedings IEEE 65th Vehicular Technology Conference (pp. 21-25). Dublin, Ireland. Haerri, J., Filali, F., Bonnet, C., & Fiore, M. (2006). VanetMobiSim: Generating Realistic Mobility Patterns for VANETs. In Proceedings of the 3rd international workshop on Vehicular ad hoc networks (pp 96—97). Los Angeles, CA, USA Huaizhi, Li, & Singhal, M. (2005) A scalable routing protocol for ad hoc networks. In Proceedings of the Vehicular Technology Conference 2005 Spring. 2005 IEEE 61st Volume 4 (pp. 2498—2503). Stockholm, Sweden Jardosh, A., Belding-Royer, Elizabeth M., Almeroth, K. C., & Suri, S. (2003). Towards realistic mobility models for mobile ad hoc networks. In MobiCom ’03: Proceedings of the 9th annual international conference on Mobile computing and networking, (pp. 217—229). ACM Press, New York, USA. Jist/SWANS (2008). Java in Simulation Time / Scalable Wireless Ad hoc Network Simulator. Retrieved March 19, 2008, from http://jist.ece. cornell.edu/index.html Johnson, D. B., Maltz, D. A. & Broch, J. (2001). DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad Hoc Networks. In C. E. Perkins (Ed.), Ad Hoc Networking (pp. 139-172). Addison-Wesley, 2001 Karnadi, F. K., Zhi, H. M., & Kun-chan, L. (2007). Rapid Generation of Realistic Mobility Models for VANET. Wireless Communications
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and Networking Conference, WCNC 2007. IEEE (pp. 2506-2511). Kowloon, Hong Kong. Karp, B., & Kung, H. T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 243-254). Boston, Massachusetts, United States. Katz, J. H. (1963). Simulation of a Traffic Network. Communications of the ACM, 6(8), 480-486 Killat, M., Schmidt-Eisenlohr, F., Hartenstein, H., Rössel, C., Vortisch, P., Assenmacher, S., & Busch, F. (2007). Enabling efficient and accurate large-scale simulations of VANETs for vehicular traffic management. In Proceedings of the Fourth ACM international Workshop on Vehicular Ad Hoc Networks, VANET ‘07 (pp. 29-38). Montreal, Canada. Krajzewicz, D., Bonert, M., & Wagner, P. (2006). The Open Source Traffic Simulation Package SUMO. Presented at RoboCup 2006 Infrastructure Simulation Competition. Bremen, Germany. Mahajan, A., Potnis, N., Gopalan, K., & Wang, A. (2007). Modeling VANET Deployment in Urban Settings. In Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems (pp 151-158). Chania, Crete Island, Greece. Mangharam, R., Weller, D. S., Stancil, D. D., Rajkumar & R., Parikh, J. S. (2005). GrooveSim: A Topography-Accurate Simulator for Geographical Routing in Vehicular Networks. In Proceedings of the 2nd ACM International Workshop on Vehicular Ad Hoc Networks (pp. 59-68). Cologne, Germany MOVE (2008). MObility model generator for VEhicular networks. Retrieved March 19, 2008, from http://www.csie.ncku.edu.tw/~klan/move/ index.htm
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ns-2 (2008). The ns-2 wiki. Retrieved March 18, 2008 at http://www.isi.edu/nsnam/ns/doc/ Petigara, N. D., & Balakrishnan, H. (2004). Integrated routing and storage for messaging applications in mobile ad hoc networks. Mobile Networks and Applications, 9(6), 595-604. Perkins, C., & Bhagwat, P. (1994). Highly Dynamic Destination-Sequence DistanceVector Routing (DSDV) for Mobile Computers. ACM SIGCOMM Computer Communication Review, 24(4), 234-244. Piorkowski M., Raya M., Lugo A., Papadimitratos P., Grossglauser M., & Hubaux J. P. (2007, September). TraNS: Realistic Joint Traffic and Network Simulator for VANETs. Short paper presented at the Fourth ACM international Workshop on Vehicular Ad Hoc Networks, VANET ‘07. Montreal, Canada. Potnis, N., & Mahajan, A. (2006). Mobility Models for Vehicular Ad Hoc Network Simulations. In Proceedings of the 44th ACM Southeast regional conference (pp 746-747). Melbourne, Florida. Qualnet (2001). Version 3.1 User’s Manual. Retrieved March 19, 2008 from http://linux.ucla. edu/~gavin/papers/SimulatorManual-3.1.pdf Ravi, N., Smaldone, S., Iftode, L., & Gerla, M. (2007). Lane Reservation for Highways (Position Paper). Retrieved March 19, 2008 from www. cs.rutgers.edu/~iftode/ILR-ITSC07.pdf Rehunathan, D. B., Seet, B. C., & Luong, T. T. (2007). Federating of MITSIMLab and ns-2 for realistic vehicular network simulation. In Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology (pp 6267). Singapore rfc3561 (2008). Ad hoc On-Demand Distance Vector (AODV) Routing. Retrieved March 19, 2008 from http://www.ietf.org/rfc/rfc3561.txt
Saha, A., & Johnson, D. (2004). Modeling Mobility for Vehicular Ad-hoc Networks. In Proceedings of the 1st ACM International Workshop on Vehicular Ad Hoc Networks (VANET) (pp. 91-92). Philadelphia PA, USA. Sklar, B. (1997). Rayleigh Fading Channels in Mobile Digital Communication Systems Part I: Characterization. IEEE Communications Magazine, 35(7), 90-100 Sommer, C., Yao, Z., German R., & Dressler, F. (2008) On the Need for Bidirectional Coupling of Road Traffic Microsimulation and Network Simulation. In Proceedings of the Computer Communications Workshops. INFOCOM (pp 1-6).Phoenix, AZ. SUMO (2008). Simulation of Urban MObility. Retrieved March 19, 2008 from http://sumo. sourceforge.net/docs/bibliography.shtml Taliwal, V., Jiang, D., Mangold, H., Chen, C., & Sengupta, R. (2004). Empirical determination of channel characteristics for DSRC vehicle-tovehicle communication. In Proc. 1st ACM Int. Workshop on Vehicular Ad Hoc Networks (p. 88). Philadelphia PA, USA. TIGER (2008). Topologically Integrated Geographic Encoding and Referencing system. Retrieved March 19, 2008 from http://www.census. gov/geo/www/tiger/ VISSIM (2008). VISSIM Micro-Simulation Software. Retrieved March 19, 2008 from http://www. english.ptv.de/cgi-bin/traffic/traf_vissim.pl Wang, S. Y., Chou, C. L., Chiu, Y. H., Tseng, Y. S., Hsu, M. S., Cheng, Y. W., Liu, W. L., & Ho, T. W. (2007, September). NCTUns 4.0: An Integrated Simulation Platform for Vehicular Traffic, Communication, and Network Researches. Paper presented at the 1st IEEE International Symposium on Wireless Vehicular Communications. Baltimore, MD, USA
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Wiedemann, R. (1974) Simulation des Strassenverkehrsflusses. Schriftenreihe des Instituts für Verkehrswesen der Universität Karlsruhe, Heft 8 Yang, Yi, Varshney, M., Mohan, S., & Bagrodia, R. (2007). High-Fidelity Application-Centric Evaluation Framework for Vehicular Networks. In Proceedings of the fourth ACM international
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workshop on Vehicular ad hoc networks (pp 4958). Montreal, Quebec, Canada Yin, J., ElBatt, T., Yeung, G., Ryu, B., Habermas, S., Krishnan, H., & Talty, T. (2004). Performance evaluation of safety applications over DSRC vehicular ad hoc networks. In Proceedings of the 1st ACM international Workshop on Vehicular Ad Hoc Networks (pp. 1-9). Philadelphia PA, USA
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Chapter XV
In-Vehicle Network Architecture for the Next-Generation Vehicles Syed Masud Mahmud Wayne State University, USA
ABSTRACT New types of communication networks will be necessary to meet various consumer and regulatory demands as well as satisfy requirements of safety and fuel efficiency. Various functionalities of vehicles will require various types of communication networks and networking protocols. For example, driveby-wire and active safety features will require fault tolerant networks with time-triggered protocols to guarantee deterministic latencies. Multimedia systems will require high-bandwidth networks for video transfer, and body electronics need low-bandwidth networks to keep the cost down. As the size and complexity of the network grows, the ease of integration, maintenance and troubleshooting has become a major challenge. To facilitate integration and troubleshooting of various nodes and networks, it would be desirable that networks of future vehicles should be partitioned, and the partitions should be interconnected by a hierarchical or multi-layer physical network. This book chapter describes a number of ways using which the networks of future vehicles could be designed and implemented in a cost-effective manner. The book chapter also shows how simulation models can be developed to evaluate the performance of various types of in-vehicle network topologies and select the most appropriate topology for given requirements and specifications. Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
In-Vehicle Network Architecture for the Next-Generation Vehicles
INTRODUCTION
body electronics. This is the reason why currently most vehicles have at least two serial buses: one for power-train and the other for body electronics, as shown in Figure 1. Since there are some mutually required data between the two partitions of the invehicle network, the partitions are interconnected by a gateway device as shown in Figure 1. The two partitions need not use the same type of bus though they can. Since the LIN (Local Interconnection Network) bus is less expensive and LIN protocol (Uplap, 2004) is less complex than the corresponding CAN (Controller Area Network) bus and CAN protocol (Motorola,1998), nowadays many vehicles are considering using a low-speed LIN bus for body electronics and a high-speed CAN bus for power-train. The gateway device which interconnects a CAN bus and a LIN bus converts messages from one protocol to another protocol when the messages need to go from one partition to another partition. As the government and consumer demands are increasing for many features such as safety, fuel efficiency, comfort, navigation, entertainment, telematics, multimedia and many more, future vehicles will have many more partitions. Each partition will take care of a particular type of application. All the partitions will need to be interconnected for exchanging mutually
Early vehicles used dedicated point-to-point connections for inter-module communications. As the number of modules and features increased in vehicles, the wiring system became bulky, complex, expensive and difficult to install and maintain. As a result, over the years, evolution of in-vehicle communications via a serial bus took place. A serial bus can replace all the dedicated point-to-point wiring between modules. Thus, it significantly reduces wiring complexity as well as weight of the vehicle making the vehicle more fuel efficient. A serial bus system is also scalable, meaning that more modules can be connected to the bus at any time without requiring any changes at other modules. Since different functions of a vehicle need different data rates, such as powertrain needs higher data rate than body electronics, the use of a single serial bus for the entire vehicle may not be the best choice to design the in-vehicle communication network. As high-speed electronic components are more expensive than low-speed electronic components, it would be desirable and cost effective to partition the serial bus into two buses: a high-speed serial bus and a low-speed serial bus. The high-speed bus can be used for power-train and the low-speed bus can be used for
Figure 1. An in-vehicle networking system with two partitions Partition-1: Power-Train Anti-Lock Brakes Module
Transmission Module
Engine Module
Accident Avoidance Module
Partition-2: Body Electronics
284
High-Speed CAN bus
Instrument Module
Door Module
Climate Control Module
Lights Module
Gateway Module
Low-Speed CAN or LIN Bus
In-Vehicle Network Architecture for the Next-Generation Vehicles
required information as well as for diagnostic purposes. Like early vehicles, dedicated pointto-point connections among various partitions will increase the complexity of connectivity, as shown in Figure 2. Figure 2 shows an in-vehicle networking system with four partitions. Each has its own bus system. Each pair of partitions is interconnected by a gateway device. For example, Partition-1 and Partition-2 are connected by the gateway device G12. Similarly, Partition-1 and Partition-4 are connected by the gateway device G14. The number of gateway devices increases exponentially with the increase in the number of partitions. For N partitions, the number of gateway devices required for dedicated connections N! . Thus, among the various partitions is 2 * ( N − 2)! dedicated connections among the partitions will be very complex and cost prohibitive. As the number of partitions in a vehicle is going to increase, the automakers must come up with less complex and less expensive techniques for interconnecting the partitions. The main objective of this book chapter is to show various cost-effective interconnection mechanisms among the partitions of an in-vehicle networking system. The advantages, disadvantages and the feasibility of various interconnection
mechanisms using the current technology are also explained in this book chapter.
BACKGROUND There is little doubt that the automobile is the most revolutionary invention in the history of transportation since the wheel. Over the last 100 years, automobiles have gone through numerous phases of improvements. At the dawn of the twentieth century, transportation of people and goods in timely and cost effective manner was the main motivation behind developing automobiles. Safety, fuel efficiency, comfort, entertainment, green house effect, etc. were not the major issues for those early automobiles. Thus, the first generation automobiles were mostly mechanical devices, and they had very few electrical and electronic components. As a result, interconnecting those few electrical and electronic components was not a major problem. After that, various new features were being added to automobiles out of necessity. Initially, the necessity of safety and fuel efficiency was the main driving force behind improvements of automobiles. Since the 1960’s, there have been significant improvements in vehicle safety. The introduction
Figure 2. Dedicated connections among four partitions of an in-vehicle networking system. Partition-1
Partition-2 G12
G14
G13
G23
G24
G34 Partition-3
Partition-4
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of safety features such as seat belts, air bags, crash zone, lighting system and new vehicle structures has dramatically reduced the rate of crashes, injuries and fatalities. However, in spite of these impressive improvements, according to U.S. Department of Transportation there were nearly 45,000 fatalities in transportation accidents in the United States in 2004, of which 95 percent involved highway motor vehicles (USDOT, 2006). By continuing with the passive safety technologies, it is difficult to achieve significant further gains in reducing crash costs. Thus, engineers and researchers have started developing active safety technologies to further reduce the costs involved with vehicle crashes. As a result, more electrical and electronic components are being added to vehicles. The energy crisis pushed the automotive industries to make their vehicles more fuel efficient. Fuel efficiency requires efficient burning of fuel inside engine cylinders which eventually requires optimal air-fuel ratio, optimal spark timing and optimal control of valves. As a result, carburetors were replaced by computer controlled electronic fuel injection systems; spark timing is controlled by computers; mechanical valves are being replaced by electrical valves; and many more, which eventually added more electrical and electronic components in vehicles. As the electrical and electronic components grew in vehicles, the dedicated point-to-point interconnection among these components became very bulky, complex and expensive. As a result, the concept of multiplexing or in-vehicle networking evolved since early 1980’s. In fact, one of the first automotive papers that addressed the need for vehicle multiplexing was published as early as 1976 (Bell, 1976). Multiplexing uses a serial bus, and all electrical and electronic components are connected to this bus via microcontrollers. The microcontrollers take active role in exchanging information among various electrical and electronic components of the vehicles. The microcontrollers send information using messages. The messages
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are created by transmitting microcontrollers and decoded by receiving microcontrollers based on a particular communication protocol used by vehicles. In the 1980’s, automotive companies were developing their own protocols and there was no standardization in networking protocols among various companies. As a result, jobs of chip manufacturers, suppliers and mechanics were becoming difficult because chip manufacturers had to make different chips for different protocols, suppliers had to make different parts to make them compatible with different protocols and the mechanics had to use different diagnostic computers for different protocols. Recently almost all automotive companies both in US and Europe have started using Controller Area Network (CAN) protocol for in-vehicle networking. Thus, nowadays almost every high end vehicle has at least one CAN network. The Society of Automotive Engineers (SAE) has defined three basic categories of in-vehicle networks based on network speed and functions: Class A, Class B and Class C networks. •
•
Class A is a low-speed network with a bit rate of 10 Kbits/sec or less. Class A network is to be used for convenience features such as entertainment, audio, trip computer, etc. Class A functions require inexpensive and low-speed communication, and typically utilize generic UARTs (Universal Asynchronous Receiver/Transmitter). Class B is a medium-speed network. Data rate for Class B network is between 10 Kbits/sec and 125 Kbits/sec. The intended application for Class B network is general information transfer such as instrument cluster, vehicle speed, legislated emissions data, etc. In the US, the SAE adopted J1850 as the standard protocol for Class B networks. SAE J1850 has two basic versions. The first is a 10.4 Kbits/sec VPW (Variable Pulse Width) type which uses a single bus wire. The second is a 41.6 Kbits/sec PWM
In-Vehicle Network Architecture for the Next-Generation Vehicles
•
(Pulse Width Modulation) type which uses a two-wire differential bus. Class C is a high-speed network, and it is to be used for real-time control applications such as power-train, vehicle dynamics, antilock brakes, etc. The data rate for Class C network is 125 Kbits/sec to 1 Mbits/sec. CAN protocol has been selected for Class C applications.
Continuous regulatory demands for fuel efficiency, safety and emission control are forcing the automakers to implement advanced safety and engine management systems. These advanced systems will require new types of networks and networking protocols. The demands for improved fuel efficiency require drive-by-wire systems that eliminate camshafts, power-sapping belt drives and pumps, and a great deal of unnecessary weight. The goal of drive-by-wire is to replace as many mechanical systems as possible by equivalent electronic and electrical systems. Electric brakes will replace hydraulics, increasing safety, decreasing weight, lowering operating cost and eliminating the use of environmentally hazardous fluids. Steering columns will disappear and will be replaced by electric steering, improving driver safety and making it easy to provide left-hand and right-hand drive models. Similarly, as the demand for other new features such as telematics, entertainment, multimedia, pre-crash warning, driver assistance systems, remote diagnostic, software update, etc. increases, the complexity of in-vehicle communication networks increases as well. Depending upon the new functionality of vehicle electronic modules, different sets of modules will require different types of new networks. For example, drive-by-wire and active collision avoidance systems need fault tolerant networks with timetriggered protocols such as TTP/C (TTTech, 2002), TTCAN (Fuehrer, 2001) and FlexRay (FlexRay, 2004), to guarantee critical services with deterministic latencies. Airbag systems need a protocol that can react very quickly. For
airbag systems there are various protocols, such as Safe-by-Wire (Lupini, 2004), Bosch Siemens Temic (BST), DSI and Byteflight (Lupini, 2004), which are being investigated. Multimedia systems need high-bandwidth networks such as MOST and IntelliBus (Lupini, 2004; Firlit, 2004), to transfer video files. Wireless personal area networks with voice activated control are necessary for convenience such as turning on/off lights, wipers, radio, heat and air conditioner without taking hands off the steering wheel (Mahmud, 2006). Bluetooth or Wi-Fi protocol could be used for wireless personal area network. Telematic systems need technologies such as Wi-Fi or Ultrawideband (UWB). Thus, it is apparent that future vehicles will have various types of networks with many partitions. Interconnecting these partitions is necessary for distributed control across various partitions and ease of diagnosis. Since automotive industry is very cost sensitive, interconnecting these partitions will be a challenging task especially when the number of partitions is large. Major studies and in-depth analysis are necessary to determine optimal topology for interconnecting all the invehicle network partitions, to figure out optimal bandwidth required from different types of network partitions for keeping latencies under certain bounds, and to select appropriate communication protocols for different network partitions. Various topologies for interconnecting different in-vehicle network partitions are investigated and presented in the next section of this book chapter.
INTERCONNECTION OF NETWORK PARTITIONS Issues, Controversies and Problems Multiplexing or in-vehicle networking evolved due to the need for reducing wiring harness, ease of integration, ease of diagnosis and cost savings. Most of the today’s vehicles have only two network partitions as mentioned earlier in
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this book chapter and also shown in Figure 1. Interconnecting two partitions is a trivial problem. We need only one gateway device to connect the two partitions. The gateway device must support protocols of both partitions. The gateway device will do protocol conversions when messages need to go from one partition to the other partition. Nowadays, microcontrollers are available that support various types of interfaces. For example, PIC18F4685 microcontroller supports SPI, I2C, RS-485, RS-232, LIN and CAN interfaces. Similarly, PIC18F97J60 microcontroller supports SPI, I2C, RS-485, RS-232, LIN and Ethernet interfaces. Thus, a microcontroller that supports both interfaces of the two partitions, shown in Figure 1, can be used as the gateway device between the two partitions. As the number of partitions grows, dedicated point-to-point connections among various partitions will require many gateway devices, and beyond a certain point it would be cumbersome and cost prohibitive. One of the challenging issues will be the selection of an appropriate topology to interconnect various partitions of the in-vehicle network.
S There are various existing network topologies such as bus, ring, mesh, crossbar, star, etc. Bus-based systems have many advantages over other interconnection systems (Das, 1985; Chen, 1999). Some of the advantages of bus-based systems are low cost, lower complexity, greater reliability, better scalability, simpler communication protocols and ease of diagnostics. Thus, in order to interconnect all partitions, one logical approach would be to use another bus such as a hierarchical bus.
Selection of a Topology In this subsection, a number of multi-level bus-based network topologies are discussed and suggested for future in-vehicle networks.
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A hierarchical bus-based network provides a cost-effective interconnection solution technique for partition-based systems (Mahmud, 1994). Since interconnection using a hierarchical bus requires less number of gateway devices, one logical approach for interconnecting four partitions of a vehicular network will be the use of a second level bus or an Level 2 bus (L2 bus), as shown in Figure 3 (Mahmud, 2005). Figure 3 shows that each partition has its own bus called the Level 1 bus or L1 bus. Different partitions have different types of L1 buses. For example, one partition may have a CAN bus as its L1 bus, and another partition may have a LIN bus as its L1 bus. An L1 bus of a partition is connected to the L2 bus by a gateway device. For example, the L1 bus of Partition-1 is connected to the L2 bus using the gateway device G1, as shown in Figure 3. Thus, the number of gateway devices required to connect N partitions to the L2 bus is N. Whereas for dedicated point-to-point connections among various partitions, the number N! . of required gateway devices is 2 * ( N − 2)! Hence, the complexity of connections shown in Figure 3 is much less than that shown in Figure 2. Figure 3 shows an additional unit called the Diagnostic Gateway. Dealer will connect its diagnostic computer (diagnostic module) to the vehicle via this diagnostic gateway. Thus, for N partitions in a system, total number of connections to the L2 bus including the diagnostic gateway is (N + 1). For a dedicated point-to-point connection system, there has to be a gateway device between the diagnostic module and each partition. Thus for a system with N partitions and a diagnostic module, dedicated point-to-point connections will ( N + 1)! gateway devices. It is clear require 2 * ( N − 1)! that a two-level bus system, which contains an L1 bus for each partition and only one L2 bus for the entire system, is a cost-effective way of interconnecting all the partitions. The bit rate of the L2 bus should be at least equal to the total bit rate
In-Vehicle Network Architecture for the Next-Generation Vehicles
necessary for inter-partition communications for all partitions. Normally not too much inter-partition data needs to be exchanged among various partitions. Thus, a Class B or even a Class A bus may be good enough. However, the final selection decision must be made after an extensive analysis and simulation of the system. Required Bandwidth for the L2 bus: An approximate analysis of the required bandwidth for the L2 bus can be determined as follows. Assume that the system has N partitions. Let di be the traffic density (bits/sec) on the L1 bus of Partition-i. Nowadays, most messages for a vehicular system are periodic. We would like to make it clear that periodic messages are not to be confused with time-triggered messages. Time slots are reserved for time-triggered messages but not for periodic messages. Periodic messages need to go through an arbitration process to get the bus. If all periodic messages and their periods for a particular partition are known, then the value of di can be estimated. Let Mi be the number of messages in Partition-i, and li,j be the length of Message-j of Partition-i with period ti,j. The value of di can then be written as:
Mi
di = ƒ
li , j t
(1)
j =1 i , j
Let f i be the fraction of Partition-i’s messages that will go to the L2 bus. Then the total traffic density on the L2 bus is N N ≈ Mi l i, j d L 2 = ƒ di f i = ƒ ∆ƒ ∆ t i =1 i =1 j =1 i , j
÷ ◊◊f i
(2)
As the traffic density on a bus increases, the queuing delay for the messages to get the bus increases as well. If u is the utilization of a particular bus, then the probability that a message will get the bus during its first trial is (1 – u). The probability that the message will not get the bus during its first trial but will get the bus during its second trial is u(1 – u). In general, it can be shown that the probability that the message will not get the bus during the first (i – 1) trials but it will get the bus during the ith trial is ui–1(1 – u). Hence, the average number of trials necessary for ∞ 1 the message to get the bus is ƒ iu i −1 (1 − u ) = . 1− u i =1
Figure 3. An in-vehicle network with four L1 buses and one L2 bus Partition-1
Partition-2
L1 Bus
L1 Bus
G1
G3
Diagnostic Gateway
L2 Bus
G2
G4
L1 Bus
L1 Bus
Partition-3
Partition-4
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In-Vehicle Network Architecture for the Next-Generation Vehicles
The average message transmission delay through the bus, including the queuing delay for the bus, is lavg . Where, lavg is the average length of a (1 − u ) BW message and BW is the bandwidth of the bus. For example, if u = 30%, lavg = 120 bits and BW = 500 kbits/sec, then the average message transmission delay including the queuing delay for the bus is ulavg 343 μsec. The average queuing delay is (1 − u ) BW and it increases very rapidly with the increase in bus utilization u. For example, for lavg = 120 bits and BW = 500 kbits/sec, the average queuing delay is 103 μsec for 30% bus utilization, and it is 360 μsec for 60% bus utilization. In vehicular networks, normally the bus utilization is kept around 30% or less to reduce contention on the bus so that a message is not significantly delayed to get the bus. Let uL2 be the maximum allowable stead-state utilization of the L2 bus. The required bandwidth for the L2 bus can then be expressed as ≈ M i li , j ÷ ◊◊f i i =1 j =1 ti , j = uL 2 N
BWL 2 =
dL2 u L2
ƒ ∆∆ƒ
(3)
Since, for a given system, the values of li,j and ti,j are accurately known, and the value of f i can also be accurately determined by investigating all the messages of a partition, the value of the required bandwidth, BWL2, for the L2 bus can be determined for a given value of uL2. However, the actual selection of the L2 bus needs to be made after extensive simulation and analysis of the entire system.
End-to-End Message Latency for a 2-Level Bus System: In a 2-level bus system, an inter-partition message will first go through the L1 bus of the source partition, then it will go through the L2 bus, and finally it will go through the L1 bus of the destination partition. Thus, various bus delays and protocol conversion delays will contribute to the total end-to-end message latency from the source node to the destination node. For a particular message, the end-to-end latency due to network components, such as buses and gateway devices, can be expressed as: T = TSL1 + TSG +TL2 + TDG + TDL1
where, TSL1 is the delay due to the L1 bus of the source partition; TSG is time required by the source gateway device (the gateway device which connects the source L1 bus to the L2 bus) to convert messages from one protocol to another protocol; TL2 is the delay due to the L2 bus; TDG is time required by the destination gateway device (the gateway device which connects the destination L1 bus to the L2 bus) to convert messages from one protocol to another protocol; and TDL1 is the delay due to the L1 bus of the destination partition. TSG and TDG can also be called as protocol conversion time. Substituting the values of bus delays in Equation (4), the end-to-end message delay can also be expressed as (see Box 1), where, lavg – SL1, uSL1 and BWSL1 respectively indicate the average message length, utilization and bandwidth of the source L1 bus; lavg – L2 , uL2 and BWL2 respectively indicate the average message length, utilization and bandwidth of the L2 bus; and lavg – DL1 , uDL1 and BWDL1 respectively indicate the average message length, utilization and bandwidth of the
Box 1. T=
290
lavg − SL1
(1 − uSL1 )BWSL1
+ TSG +
lavg − L 2
(1 − uL 2 )BWL 2
(4)
+ TDG +
lavg − DL1
(1 − uDL1 )BWDL1
(5)
In-Vehicle Network Architecture for the Next-Generation Vehicles
destination L1 bus. The message delay basically has two components: the sum of the delays due to all buses and the sum of the protocol conversion times for the gateway devices. Thus, the message delay for a two-level bus system can be expressed as, (see Box 2). Since the number of gateway devices in a twolevel bus system increases linearly, as opposed to exponential increase for dedicated point-to-point connections, a two-level bus system is scalable meaning that the system is easily expandable. Although the system shown in Figure 3 is scalable, the number of connections on the L2 bus will be too many if there are too many partitions in the in-vehicle network. A bus with a given speed can support up to a certain number of nodes and up to a certain length due to various reasons as explained in the next few sentences. As the number of nodes increases, the capacitive loading effective on the bus increases due to connections which eventually increases the rise-time and fall-time of the electrical signals. As a result, the maximum bit rate capacity of the bus decreases. When more nodes are added to a bus, the total bandwidth demand from all the nodes increases as well. Thus, for a certain allowable bus utilization, the bus will be able to support up to a certain number of nodes. For example, if the bandwidth of a bus is 500kbits/sec and the allowable bus utilization is 30%, then total bandwidth demand from all the nodes can’t exceed 150kbits/sec. If each node needs a bus bandwidth of 10kbits/sec, then the bus can support only up to 15 nodes. Lastly, when the length of the bus increases, the capacitive loading effective on the bus also increases. As a result, the rise-time and fall-time of the signals increase, especially for the end-to-end communication on
the bus. Hence, the maximum bit/rate capacity of the bus decreases. Future vehicles many require many partitions, one for each subsystem of the vehicle. For example, the airbag system will need a partition for itself because there could be as many as 64 nodes for the airbag system (Lupini, 2004). In future, as the number of partitions will grow, various inter-partition connection topologies should be investigated in order to select an appropriate topology for interconnecting partitions. The remaining parts of this book chapter present various topologies for interconnecting partitions. Figure 4 shows another design where, for the same number of partitions in the system, the number of connections to the L2 bus is less than that shown in Figure 3. Figure 4 shows that six partitions (P1, P2, . . , P6) are connected to the L2-bus using three gateway devices (G1, G2, and G3). Each gateway device has three ports: two for connecting to two partitions and one for connecting to the L2-bus. As mentioned earlier, nowadays commercial microcontrollers are available that support many different interfaces. Thus, for the gateway devices, G1, G2 and G3, microcontrollers must be selected in such a way that they support necessary interfaces for the L1 and L2 buses. The system shown in Figure 4 also has a diagnostic gateway (DG) connected to the L2 bus. For the design shown in Figure 4, total number of connections to the L2 bus including the diagnostic gateway isN/2+1, where N is the number of partitions and the notation n means ceiling of n. Moreover, for the same number of partitions in the system, the data rate required for the L2 bus of Figure 4 is less than that for the L2 bus of Figure 3. The reason is that the data
Box 2. T=
≈ lavg −bus ∆ ∆ over − two − L1− buses (1 − ubus ) BWbus and − one − L 2 − bus
ƒ
÷ ◊◊ + ƒ TPr otocolConversion (6) − two − gateway over devices
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In-Vehicle Network Architecture for the Next-Generation Vehicles
transfer between the pairs of partitions (P1, P2), (P3, P4) and (P5, P6) can go directly through the gateway devices rather than through the L2 bus. As a result, for the same L2 bus technology, the design shown in Figure 4 can support more partitions than that shown in Figure 3. In order to design a system like the one shown in Figure 4, a partition, say Pi, should be paired with another partition, say Pj, such that inter-partition traffic between the pair (Pi, Pj) will be more than that between any other pair (Pi, Pk) for all j ≠ k. In other words two partitions can be paired together if they have more functional dependencies. Moreover, the two partitions which are going to be paired together should be physically close to each other. Otherwise, very long wires will be necessary to connect them to the corresponding gateway device which will reduce the benefits of multiplexing. In the recent past, drive-by-wire or x-by-wire concepts have drawn significant attentions within the automotive industries. As drive-by-wire may become a standard sometimes in the future, various safety critical messages may go through one for more L1 buses. To guarantee on-time delivery of safety critical messages, future in-vehicle networks should be designed such that the system will tolerate bus faults. Otherwise, the safety of vehicles and passengers will be compromised. There are various existing time-triggered protocols for drive-by-wire applications. Some of
these protocols are TTCAN, FlexRay and TTP/C. FlexRay and TTP/C support a dual-bus system. In these systems, safety critical messages are sent through both buses at the same time. Thus, safety critical messages can still be delivered if only one bus is at fault at the time of delivery. If the TTCAN protocol is ever going to be used for drive-by-wire applications, then it must also support two buses for tolerating single-bus faults. Figure 5 shows another network architecture where Partition P1 contains a network with a timetriggered protocol for drive-by-wire applications. P1 uses two buses to tolerate single-bus faults. As a result, the gateway device G1 has four ports: two for two L1 buses of P1, one for one L1 bus of P2 and another one for the L2 bus. As mentioned earlier, pairing of partitions will be feasible if the two partitions of a pair are close to each other, and they have significant functional dependencies. Otherwise, pairing will not provide any additional benefits to vehicle multiplexing. If pairing is not possible due to functional or physical limitations, and if one L2 bus is not enough to connect all partitions of a vehicular network, then two or more interconnected L2 buses can be used. The architecture shown in Figure 6 has two L2 buses. Each L2 bus connects a number of partitions to form a cluster of partitions. One L2 bus connects Partitions P1 through Pi to form one cluster, and another L2 bus connects Partitions Pi+1 through Pj to form another cluster. The
Figure 4. An in-vehicle network with six partitions (P1, P2, . . , P6), three gateways/switches (G1, G2 and G3) and one diagnostic gateway (DG) P1
P2
G1
P4
P5
G2 L2 Bus
292
P3
G3
DG
P6
In-Vehicle Network Architecture for the Next-Generation Vehicles
Figure 5. An in-vehicle network with six partitions, three gateways/switches and one diagnostic gateway. Partition P1 has two L1 buses P1
P2
P3
G1
P4
P5
G2
P6
G3
L2 Bus
DG
Figure 6. An in-vehicle network with two clusters of partitions. A gateway device G connects the two clusters and a diagnostic gateway device DG P1
G1
P2
G2
Pi+1
Pi
Gi+1
Gi L2 Bus
A Cluster of Partitions
two clusters are connected to a 3-port gateway device G. The third port of G is connected to the diagnostic gateway DG. In this type of systems, inter-cluster messages will have more end-to-end latencies than inter-partition messages within the same cluster because the inter-cluster messages will have to go through one more L2 bus and one more gateway device G. Since clustering has to be done in such a way that inter-cluster messages are not going to control vehicle dynamics in real-time, some additional latency for inter-cluster messages could be tolerated by the system. If the in-vehicle networking system is going to have more than two clusters, then the additional clusters can be connected in a cascade manner. Figure 7, shows an in-vehicle networking system
G DG
Pi+2
Gi+2
Pj
Gj L2 Bus
A Cluster of Partitions
with four clusters connected in a cascade manner. The four L2 buses of four clusters are connected in a cascade manner using three gateway devices: GW1, GW2, and GW3. The end-to-end message delay will be maximum when messages need to go from Cluster 1 to Cluster 4 and vice versa. The maximum delay can be expressed as, (see Box 3). The total number of gateway devices, including the diagnostic gateway, in an in-vehicle network with cascaded clusters is m + c, where m is the number of partitions, and c is the number of clusters in the system. If the inter-cluster message delay between Cluster 1 and Cluster 4 is too high and not acceptable, then another way of interconnecting those
293
In-Vehicle Network Architecture for the Next-Generation Vehicles
Box 3. TCascade =
≈ lavg −bus ∆ ∆ over − two − L1− buses (1 − ubus ) BWbus and − four − L 2 − buses
ƒ
÷ ◊◊ + ƒ− gateway TPr otocolConversion over − five devices
(7)
Box 4. THierarchical =
≈ lavg −bus ∆ ∆ over − two − L1− buses , (1 − ubus ) BWbus two − L 2 − buses
ƒ
and − one − L 3− buses
clusters would be the use of a third level bus or an L3 bus. Figure 8 shows a system with four clusters interconnected by an L3 bus. The four clusters are connected to the L3 bus using four gateway devices: GW1, GW2, GW3 and GW4. The topology shown in Figure 8 is in fact a hierarchical network topology with three levels. For the network shown in Figure 8, the inter-cluster message delay can be expressed as, (see Box 4). The total number of gateway devices, including the diagnostic gateway, for a 3-level hierarchi-
÷ TPr otocolConversion ◊◊ + ƒ (8) − four − gateway over devices
cal in-vehicle network is m + c + 1, where m is the number of partitions, and c is the number of clusters in the system. From Equations (7) and (8), it is seen that the maximum end-to-end message delay for the hierarchical network is less than that for the cascaded network because for the cascaded network with four clusters, in the worst case the messages have to go through six buses, and five gateway devices will have to run protocol conversion algorithms. Whereas, for the hierarchical network with four
Figure 7. An in-vehicle network with four clusters connected in a cascade manner using gateway devices: GW1, GW2, and GW3 Cluster 1 P2
P1
G1
G2
Pi
Gi+1
Gi
GW1
L2 Bus
Cluster 2 Pi+2
Pi+1
Gi+2
Pj
Gj
L2 Bus
DG L2 Bus Gk+1
Pk+1
Pk+2 Cluster 4
294
L2 Bus
GW3
Gk+2
Gj+1
Gm
Pm
GW2
Gj+2
Pj+1
Gk
Pj+2 Cluster 3
Pk
In-Vehicle Network Architecture for the Next-Generation Vehicles
Figure 8. A hierarchical in-vehicle network with four clusters interconnected using an L3 bus Cluster 1 P2
P1
G1
G2 L2 Bus
Gk+1
L3 Bus
Pm
Figure 9. An in-vehicle ring network with four clusters Cluster 1 L2 Bus
Cluster 2 GW1
GW4
L2 Bus
DG L2 Bus
Cluster 4
GW3
L2 Bus Gj+1
Gm
Pk+2 Cluster 4
Gi+2 L2 Bus
DG
GW3
Gk+2
Pk+1
Gi+1
Gi
GW1
L2 Bus
GW2
L2 Bus
Cluster 3
clusters, the messages have to go through five buses, and four gateway devices will have to run protocol conversion algorithms. For the hierarchical network, the price that we have to pay to get lower message delay compared to the cascaded network, is the cost of an extra gateway device and the cost of an extra bus (the L3 bus). Thus, if little bit longer inter-cluster message delay is not a major issue, then cascaded in-vehicle network
Cluster 2 Pi+2
Pi+1
Pi
Gj
GW2 GW4
Gj+2
Pj+1
Pj
Gk
Pj+2 Cluster 3
Pk
will be a cost-effective choice than a hierarchical in-vehicle network. If Clusters 1 and 4 are physically close to each other, then another better way of forming the network will be to connect the L2 buses of Clusters 1 and 4 by a gateway device as shown in Figure 9. As a result, a ring network is formed at the level of L2 buses. For the ring network, in the worst-case an inter-cluster message will have to go through two L1 buses, three L2 buses and four gateway devices. Thus, the worst-case message delay can be written as, (see Box 5). From Equations (7), (8) and (9), it is seen that the worst-case inter-cluster message delay for the ring network of Figure 9 is less than that for the cascaded network of Figure 7 but comparable to that for the hierarchical network of Figure 8. However, the ring network will be less expensive than the hierarchical network because it does not need the L3 bus. There is one minor disadvantage of the ring network compared to the hierarchical network. In the hierarchical network, the messages
295
In-Vehicle Network Architecture for the Next-Generation Vehicles
Box 5. TRing =
≈ lavg −bus ∆ ∆ over − two − L1− buses (1 − ubus ) BWbus and − three − L 2 − buses
ƒ
from the diagnostic gateway to a network node will have to go through three buses: the L3 bus, an L2 bus and an L1 bus, and two gateway devices. Whereas, in the ring network, for the worst-case, the messages from the diagnostic gateway to a network node will have to go through three buses: two L2 buses and an L1 bus, and three gateway devices. Since a slight increase in latency during a diagnostic procedure is not a problem, a ring network will be the choice among the all three choices: cascade, hierarchical and ring, provided the physical locations of the clusters enable a ring network to be formed.
SMULATION MODELS FOR ANALYSF VARIOUS IN-VEHICLE NETWORK TOPOLOGIES Extensive simulation and analysis should be done before a particular topology is selected for a particular type of vehicle. The performance of various partitions of the in-vehicle network as well as the entire network can be determined by developing simulation models. After executing the simulation models on computers, one can determine the minimum requirements for different partitions of the network architecture. Some of these requirements will include minimum processing powers needed from various electronic modules, minimum bandwidth needed from various buses, minimum computation power and minimum size of buffers needed in various gateways, etc. Computer simulations will also allow us to select the optimal network topology needed to meet the requirements of the next
296
÷ ◊◊ + ƒ− gateway TPr otocolConversion over − four devices
(9)
generation vehicles. Types of simulations and techniques of developing simulation models for vehicular networks are described in the following subsections of this book chapter. Types of Simulations: Computer simulations can be divided into two types (Molloy, 1989): i) time-based simulation and ii) event-based simulation. In a time-based simulation, the program control loop is associated with time. For each execution of the main control loop, the simulation clock advances by a fixed time unit. In the case of an event-based simulation, the execution of the main loop represents a single event. The simulation clock is simply advanced by the amount of time since the last event. For asynchronous systems, the event-based simulation is computationally more expensive and accurate than the time-based simulation. But, for synchronous systems, the time-based simulation is as good as the event-based simulation, and it is computationally less expensive than event-based simulation. CAN protocol is an event-based protocol. Thus, an event-based simulation tool will be the most appropriate tool for analyzing various performance parameters of a CAN bus. Buses that use time-triggered protocols, such as TTCAN, and FlexRay, carry both time-triggered and event-triggered messages. The time-based simulation can be used to simulate time-triggered messages and the event-based simulation can be used to simulate event-triggered messages. Thus, a combination of both time- and event-based simulations is necessary to simulate a bus like TTCAN or FlexRay. Developing an Event-Based Simulation Tool: Here we explain how to develop an event-based simulation tool to meet the requirements of specific
In-Vehicle Network Architecture for the Next-Generation Vehicles
needs for the next generation vehicles. In a vehicle system, an event is triggered by a sensor or by the driver’s action such as pressing the brake or the gas paddle. The event then goes to a processor queue, as shown in Figure 10. If the processor is busy or other events are waiting in the processor queue, then the event stays in the processor queue. Otherwise, the processor starts working on the event immediately. The event, queued in the processor queue, will stay in the queue until the previous events are taken care of by the processor. The processor spends some time (processing time) on an event. After that, the processor tries to send a message through the bus (say CAN bus) connected to the processor. If the bus is available, then the message is immediately sent to the bus. Otherwise, the message is queued in the transmit buffer of the node. The message will go through the bus after the previous messages have been transmitted. After a certain time (transmit time), the message is queued to the receive buffer of the destination node. If the processor at the receiving node is free, then it will immediately process the message. Otherwise, the processor will first take care of other queued messages and then it will process this new message. Figure 10 shows the entire process of information flow from a sensor to an actuator for a single-bus system.
Components of the latency of an event: There is a delay (latency) between the time an event occurs at a sensor or due to a driver’s action, and the time an actuator takes an action. The latency has several components as shown in the following equation. latency = tpq + tps + ttb + tbus + tpropagation + trb + tpd (10) where, tpq = delay at the processor queue tps = processing time at the source node ttb = delay at the transmit buffer of the source node tbus = transmit time through the bus tpropagation = propagation delay through the bus trb = delay at the receive buffer of the destination node tpd = processing time at the destination node
• • • • • • •
In the simulation model, the value of the delay, tpq, at the queue of the source processor can be determined by monitoring the arrival and exit times of the event in the processor queue. The value of
Figure 10. Information flow from a sensor to an actuator Transmit Buffer of CAN node
Processor Queue Sensor
Processor CAN Bus
Receive Buffer of CAN node Processor
Actuator
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In-Vehicle Network Architecture for the Next-Generation Vehicles
the processing time, tps, depends on the type of the processor. This time can be expressed as tps = k1s + k2sN
(11)
Where k1s is a constant time, which represents the constant overhead of the processor at the source node in order to prepare a message. This constant overhead may include: • •
•
•
The time needed by the processor to get out of the main program, The time needed by the processor to go to the interrupt service routine (assuming that the transmission of a message is interrupt driven), The time needed by the processor to check some status bits and set/clear some control bits of the CAN controller, The time needed by the processor for some other house keeping operations.
Thus, for a given processor, the value of k1s can be calculated based on its clock frequency and the number of instructions the processor needs to execute the abovementioned operations. The parameter k2s, shown in Equation (11), is another constant, per byte of data in coding the message, and N is the number of data bytes in the message. The value of ttb can be determined by monitoring the arrival and exit times of the message in the transmit buffer of the source node. The value of tbus can be expressed as tbus =
L S
(12)
Where, L is the total number of bits in the message, and S is the speed of the bus in bits/sec. For example, if the message is a CAN message, then L will include the start bit, arbitration bits, RTR bit, control bits, data bits, stuff bits, CRC bits, all delimiter bits and the end of frame bits.
298
The value of tpropagation should be negligible compared to other components of the delay. It depends on the speed of electrical signals through the bus. In free space, electromagnetic signals take approximately one nanosecond to move a foot. Thus, the propagation delay through a bus should be little over one nanosecond per foot. As a result, the value of tpropagation should be in the order of few tens of nanoseconds which is very small compared to other components of Equation (10). Thus, the propagation delay through the bus can be ignored for performance analysis. The value of trb can be determined by monitoring the arrival and exit times of the message in the receive buffer of the destination node. Like tps, the value of tpd can be expressed as tpd = k1d + k2dN
(13)
Where k1d is a constant time, which represents the constant overhead of the processor at the destination node in order to decode a message, and k2d is the additional overhead, per byte of data, in decoding the message. For a system with multiple partitions, the latency of an event can be expressed as, (see Box 6). The term tgateway indicates the delay at each gateway device on the path of the signal. There are three components of tgateway: queuing delays at both the input and output queues of the gateway and the time required for protocol conversion by the gateway device. Simulation Model: Figure 11 shows the block diagram of an event-based simulation model. In an event-based simulation model, an event is generated by a scheduler. The scheduler generates this event based on some kind of realistic stochastic process. We can develop a stochastic model for a particular event by collecting a large set of real data for that event. For example, the stochastic model of the brake event (pressing the brake paddle by the driver), can be developed by
In-Vehicle Network Architecture for the Next-Generation Vehicles
Box 6. latency = t pq + t ps + ttb +
ƒ
over − all − buses − on − the path − of − the − signal
(t
bus
+ t propagation )+
logging the driver’s brake activity over a long period of time and collecting similar data from many vehicles. After generating an event, the scheduler keeps that event in the event queue, as shown in Figure 11. When an event is dispatched from the event queue, another event is generated. This new event is then sent to the event queue. When a new event enters into the event queue, it is sorted within the event queue using the event-time as the sorting key. The new event is then placed at an appropriate point in the event queue. After dispatching an event from the event queue, the event is then sent to the event processing unit. The event processing unit then simulates the event in exactly the same way as a real system would process it. The stochastic model of an event determines the inter-arrival time of the event, based on the behavior of the device that generates the event. A synchronous device, such as a time-triggered device, generates events after fixed interval of time. Hence, for a time-triggered system, the inter-arrival time is fixed. But, an asynchronous device doesn’t generate events after fixed interval of time. Most physical asynchronous systems behave like a Poisson process. Thus, we can assume that a non time-triggered device will behave like a Poisson process. The stochastic model of an event will have the appropriate values of the
ƒ
over − all − gateways − on the − path − of − the − signal
t gateway + trb + t pd (14)
parameters that define the characteristics of the Poisson process of the event. As mentioned earlier, these parameters of the stochastic model for an event can be determined by collecting a large set of field data for that particular event. The model shown in Figure 11 can be used to simulate an event-triggered system with an event-triggered bus (say CAN bus). Since both TTCAN and FlexRay support timeand event-triggered messages, we can develop the simulation models of these buses by building another layer on the top of the event-based simulation model. Figure 12 shows a simulation model of a combined time- and event-based system. Here, the event scheduler is a deterministic as well as a stochastic process. In this case, a global simulation clock is maintained to keep the timetriggered events in sync. The frequency of this global simulation clock (software clock) should be at least equal to the highest frequency clock used in the in-vehicle network architecture. This requirement on the software clock will allow us to determine the latency and other performance parameters at a very high resolution (within a clock cycle of the highest frequency clock of the real system). At every global simulation clock pulse, the event queue must be checked. If the event-time of the event at the head of the event queue is less than or equal to the current value of
Figure 11. Model for an event-based simulation Event Scheduler (an asynchronous process)
Queue for events Event Dispatcher
Event Processor
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In-Vehicle Network Architecture for the Next-Generation Vehicles
Figure 12. Model for an event- and time-based simulation Global Simulation Clock Event Scheduler (asynchronous and synchronous processes)
Queue for events
the global simulation clock, then the event will be dispatched from the event queue. The dispatched event will then be checked to determine whether it is a deterministic event or a non-deterministic event. If it is a deterministic event, then the deterministic scheduling process will be used to schedule the next deterministic event. Otherwise, stochastic scheduling process will be used to schedule the next non-deterministic event. The scheduled event, whether deterministic or nondeterministic, will then be sent to the event queue. The event will be placed in the event queue after sorting it with other events in the event queue. The event-time will be used as the key to sort the events in the event queue. By developing a simulation model, one can determine various performance parameters of the in-vehicle network. These parameters will include, but not limited to, the latency of different types of message, busload, bandwidth, and throughput.
FUTURE TRENDS There is no doubt that future trends are going to be in the direction of drive-by-wire, active-safety, inter-vehicle communications and vehicle-toinfrastructure communications. Future vehicles will access internet and exchange information with other vehicles and road-side units for various reasons such as collision avoidance, message
300
Event Dispatcher
Event Processor
dissemination, software upgrade in electronic modules, receiving data from content providers, sharing files, etc. Some researchers have proposed architecture for Next Generation Vehicle Network and presented a dynamic discovery service protocol for internet access (Baroody, 2005; Baroody, 2006). If future in-vehicle networks are not properly designed, then vehicles’ safety could be compromised or its operation could be degraded due to various types of cyber attacks from external entities. The in-vehicle network that is mainly responsible for controlling vehicle dynamics, must be well protected from outside cyber attacks. Good security software and firewalls must be installed in all gateway devices that exchange vehicle’s dynamic information. The gateway device that connects the telematic unit to the internal network of the vehicle must have a good firewall so that it can filter out all suspicious information that is coming from outside. After extensive research, if it is not possible to come up with a good security solution to protect vehicles’ safety-critical modules, then no external traffic should be allowed to enter the safety-critical modules. For collision avoidance applications, a vehicle will disseminate its dynamic information. However, current in-vehicle networks are not secure. This means that when receiving nodes are picking up CAN messages from a CAN bus, they are not verifying whether or not the messages
In-Vehicle Network Architecture for the Next-Generation Vehicles
are authentic. The receiving nodes automatically assume that the messages are valid as long as the messages are free from various types of errors such as CRC error, bit error, stuff error and frame error. Thus, if a person with malicious intent connects a device to the vehicle’s CAN bus and sends messages with wrong information, other nodes will still accept them as valid messages. The problem will occur when these messages with wrong information are picked up by a gateway device and then routed to the vehicle’s telematic unit. The telematic unit will then broadcast these messages with wrong information to other vehicles. As a result, there could be disruption of traffic leading to accidents. Since vehicle-to-vehicle communication is a peer-to-peer communication, protection of drivers’ or owners’ privacy from peers is going to be another challenging task. Privacy protected secure communication is not an easy task. Good Cyber-Physical Systems at the network level are necessary so that future vehicles will be able to communicate with external entities without getting involved in safety, security and privacy problems. The issues related to vehicular network security are still at the research stage. The purpose of this book chapter is not to provide any security solutions but to explain the significance of this topic and what needs to be done to safely operate future intelligent vehicles. Many researchers have already started looking into these issues and are trying to propose solutions (Hossain, 2007; Rabadi, 2007; Rabadi, 2008).
CONCLUSON In this paper, we discussed various types of topologies and protocols that could be used in the future in-vehicle networks. Due to the complexity of the future in-vehicle networks, many partitions of the network will be required. These partitions
must be interconnected together for functional dependencies and better diagnostic purposes. A number of network topologies has been presented and analyzed for cost, bandwidth and message latencies. Since future vehicles will be communicating with external entities for various reasons, the book chapter also addressed the issues of security, safety and privacy which should be taken into consideration at the time of designing the in-vehicle network components. Finally, some ideas have been presented in developing simulation models to analyze various types of networks which will ultimately help in selecting a network topology and various network components for a given set of requirements and specifications.
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Firlit, T., Sandoval, J., & Ellerbrock, P. (2004). IntelliBusTM Protocol:Flexible and Cost-Effective Automotive Bus. Techincal Report, Aeroflex Colorado Springs. FlexRay Consortium (2004). FlexRay Communications System Protocol Specification. Version 2.0. Fuehrer, T., Mueller, B., Hartwich, F., & Hugel, R. (2001). Time-Triggered CAN (TTCAN). Proceedings of the 2001 SAE World Congress, Detroit, Michigan, Paper Number 2001-01-0073. Hossain, I., & Mahmud, S. M. (2007). Analysis of Group Key Management Protocols for Secure Multicasting in Vehicular Software Distribution Network. Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007), Crowne Plaza Hotel, White Plains, New York, USA, October 8-10, 2007. Hossain, I., & Mahmud, S. M. (2007). Analysis of a Secure Software Upload Technique in Advanced Vehicles using Wireless Links. Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA, Sept. 30 - Oct. 3, 2007, pp. 1010-1015. Lupini, C. A. (2004). Vehicle Multiplex Communication: Serial Data Networking Applied to Vehicular Engineering. Warrendale, Pensylvania: SAE. ISBN 0-7680-1218-X. Mahmud. S. M. (1994, July). Performance Analysis of Multilevel Bus Networks for Hierarchical Multiprocessors. IEEE Transactions on Computers, 43(7), 789-805. Mahmud, S. M., & Alles, S. (2005). In-Vehicle Network Architecture for the Next-Generation Vehicles. Proceedings of the 2005 SAE World
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About the Contributors
Huaqun Guo obtained BEng and MEng from Tianjin University in 1989 and 1991 respectively, and obtained M.Eng and PhD from the National University of Singapore (NUS) in 2001 and 2007 respectively. She was a senior engineer, Kent Ridge Digital Labs (KRDL), Singapore from 2000 to May 2001. She was a senior research staff, NUS from June 2001 to March 2003. She was an Ordinary Member of Singapore Computer Society from 2000 to 2005. She is an IEEE member from 2007, a committee member of IEEE SG WIE AG 2008, and a member of SingAREN (Singapore Advance Research and Education Network) from 2000. Dr. Guo is currently a research fellow at the Institute for Infocomm Research (I2R), Agency for Science Technology and Research (A*STAR) in Singapore. She has published number of papers in the international journals and conferences. Her research areas include multicast, vehicular network, P2P computing, security, networked middleware, and multimedia communication system. *** Omar Álvarez-Cardenas received the MSc degree in telematics at the University of Colima in 1999. He is finishing the PhD in telecommunications at the Jose Antonio Echeverria Institute from La Havana. Since 1998 he has been teaching networking at the School of Telematics where he is a reseachprofessor in telecommunications. His research focuses on network simulations, QoS in metropolitan ethernet networks, mobile computing and bandwidth efficiency. Sohel Anwar is an assistant professor in the Department of Mechanical Engineering at School of Engineering and Technology, Purdue University Indianapolis, Indianapolis, Indiana, USA. He received his PhD in mechanical engineering in 1995 from the University of Arizona, Tucson, Arizona, USA. Dr. Anwar worked for Caterpillar, Inc. from 1995 to 1999 where he was involved in developing novel X-bywire control algorithms for wheel loaders. In 1999, he joined Ford Motor Company / Visteon Corporation where he focused on developing advanced fault tolerant control algorithms for drive by wire systems. Dr. Anwar holds 12 US patents and several foreign patents. He has published over 40 papers in refereed journals and conference proceedings. He is an associate editor for IEEE Transactions on Vehicular Technology. His research interests include fault tolerant drive by wire systems, hybrid electric vehicle control, automotive control systems, and mechatronics. He may be contacted at:
[email protected]. Raúl Aquino-Santos graduated from the University of Colima with a BE in electrical engineering, received his MS degree in telecommunications from the Centre for Scientific Research and Higher Education in Ensenada, Mexico in 1990. He holds a PhD from the Department of Electrical and Electronic
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About the Contributors
Engineering of the University of Sheffield, England. Since 2005, he has been with the College of Telematics, at the University of Colima, where he is currently a research-professor in telecommunications networks. His current research interests include wireless and sensor networks. Giorgio Calandriello holds a MSc degree in informatics and system engineering and a PhD in computer engineering. Currently he’s research assistant at Politecnico di Torino in the TORSEC group, mainly working on security of vehicular communication. His research interests include wireless systems security, denial-of-service attacks and vehicular networks security and safety. Valentin Cristea is a professor of the Computer Science and Engineering Department of University Politehnica of Bucharest, Romania. The main fields of expertise include distributed systems, vehicular networks, high performance computing, and collaborative environments. He has a teaching experience of over 30 years in these domains and published more than 25 books, 130 specialist articles, and 40 scientific reports. Cristea coordinated several projects in mobile and vehicular computing, simulation of large scale distributed systems, and VANETs simulation. He co-authored several papers on designing and simulating VANET applications. He is a member of IEEE and ACM Society. Raluca Diaconescu is a software engineer working for IBM in Bucharest, Romania. She obtained an MSc degree in 2008 and a BSc degree in 2006 from “Politehnica” University Bucharest, computer science faculty. She studied VANET applications for her graduation thesis and co-authored several research papers in the field of VANETs. Her research topics include VANET simulators and collaborative route planning. Lili Du was born in Luoyang, China, in 1975. She received the BS degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 1998, and the MS in industrial engineering from Tsing Hua University, Beijing, China, in 2003. And then she received the MS in operation research and statistics and PhD degree in decision sciences and engineering systems from Rensselaer Polytechnic Institute (RPI), Troy, NY, in 2008. In September 2008, she joined NEXTRANS in Purdue University as a research associate. Her main research interests are vehicular ad hoc networks (VANETs), on-line routing, network design under disasters condition, and transportation network modeling. Arthur Edwards-Block received his masters degree in Education from the University of Houston in 1985. He has been a researcher-professor at the University of Colima since 1985, where he has served in various capacities. He has been with the School of Telematics since 1998. His primary areas of research are computer assisted language learning (CALL), distance learning, collaborative learning, multimodal leaning and mobile learning. The primary focus of his research is presently in the area of mobile collaborative learning. Miguel A. Garcia-Ruiz graduated in computer systems engineering and obtained his MSc in Computer Science from the University of Colima, Mexico. He received his PhD in Computer Science and Artificial Intelligence from the School of Cognitive and Computing Sciences, University of Sussex, England. He took a virtual reality course at Salford University, England and a graphics techniques internship at the Madrid Polytechnic University, Spain. Miguel has been a visiting professor at the University Of Ontario Institute Of Technology, Canada. He has been teaching computer science courses, and doing research
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About the Contributors
mainly on virtual reality and multimodal interfaces at the University of Colima. Miguel has published various scientific papers in major journals and a book, and has directed a video documentary about an introduction to virtual reality. Denis Gingras received his BS and MS degrees from Université Laval in 1980 and 1984 respectively, and his Dr. Eng degree, in 1989, from Ruhr-Universität Bochum, Germany, all in electrical engineering. Awarded a STA fellowship in Japan, he was invited researcher at CRL, Tokyo, from 1989 to 1990. From 1990 to 1998, he directed the digital and optical systems department at INO, in Canada. In 1999, he was invited professor at INRIA in Sophia-Antipolis, France. Since 2000, he is professor at Université de Sherbrooke, where he has also been director of the Intelligent Materials and Systems Institute from 2000 to 2008. Dr. Gingras has published two books and more than eighty publications. His research interests cover intelligent systems and information processing. Dr. Gingras was leader of the Intelligent Vehicles & Intelligent Vehicle-Highway Systems Working Group within the ATLANTIC Canada International Network and is leading the Intelligent Systems and Sensors research program in the Canadian AUTO21 NCE. Apolinar Gonzalez-Potes holds a PhD from Polytechnic University of Valencia, Spain. Since 2004, he has been with the Faculty of Mechanical Electrical Engineering of the University of Colima, where he is currently a research-professor in informatics. His current research interest includes mobile computing and embedded software. Cristian Gorgorin is a software engineer working for S&T in Bucharest, Romania. He obtained an MSc degree in 2008 and a BSc degree in 2006 from “Politehnica” University Bucharest, Computer Science Faculty. He studied VANET routing protocols for his graduation thesis and co-authored several research papers in the field of VANETs. His research topics include VANET simulators and algorithms for intelligent traffic lights. Wilfried Gouret received the MSc degree in electrical engineering from University of Rennes, France in 2004 and and the PhD degrees from National Institute of Applied science (INSA), Rennes, France in 2007. From 2004 to 2007, he was with the research laboratory IETR (Institute of Electronics and Telecommunications of Rennes) where he worked on the optimization of power line communications for automotive application. A demonstrator has been realized and demonstrates the feasibility of such system. Currently, he is research engineer and his current research focuses on high data rate wireless communications Victor Gradinescu is a software developer for S&T Romania. He has received a BSc degree and MSc degree in computer science from Politehnica University of Bucharest. His research interests have focused on the design and simulation of VANET applications, such as intersection cooperation and adaptive traffic lights, with a particular emphasis on mobile wireless networks simulation, analysis and optimization. Todd Hubing is the Michelin Professor of Vehicular Electronics at Clemson University. He holds a BSEE from MIT, an MSEE from Purdue, and a PhD from North Carolina State University. He began his career as an EMC engineer for IBM in 1982, where he did EMC testing and troubleshooting on a
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About the Contributors
variety of computer and network communications products. In 1989, he became a faculty member at the University of Missouri-Rolla (UMR) where he worked with other faculty and students to analyze and develop solutions for a wide range of EMC problems affecting the electronics industry. Since coming to Clemson in 2006, he has continued his work in electromagnetic compatibility and computational electromagnetic modeling, particularly as it is applied to automotive and aerospace electronics. Dr. Hubing is a fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a past president of the IEEE Electromagnetic Compatibility Society. Javier Ibañez-Guzmán obtained his PhD at the University of Reading on a SERC-UK assistantship and his MSEE at the University of Pennsylvania (USA) as a Fulbright scholar. He is currently working for Renault S.A where he is responsible for localisation technologies used in advanced driving assistance systems. Dr Ibañez-Guzmán has worked as senior scientist at SimTECH, a national research institute in Singapore, where he spearheaded research on autonomous ground vehicles for unstructured environments. He was responsible for several projects sponsored by the ministry of defence. Concurrently, he was adjunct associate professor at Nanyang Technological University. His research interests are in localisation and perception for intelligent vehicles. He has worked in manufacturing robotics in France and was Maître de Conferences at the engineering school ESA IGELEC (part of the Ecole Polytechnique de l’Université de Nantes). Dr. Ibañez-Guzmán is Fellow of the Institute of Engineering Technology and a Chartered Electrical Engineer (C. Eng.) of the Engineering Council of the U.K. He is member of several societies within the IEEE. Liviu Iftode is an associate professor of computer science at Rutgers University, New Jersey. He received his PhD in computer science from Princeton University in 1998. His research interests include distributed systems, operating systems, vehicular networks, mobile and pervasive computing. He is a senior member of the IEEE Computer Society and a member of the ACM. Liviu Iftode is the vice-chair of IEEE Technical Committee on Operating Systems and a member of the editorial board of IEEE Pervasive Computing. In the area of vehicular computing, Dr. Iftode led the TrafficView research project, one of the first traffic monitoring and warning system based on cat-to-car communication, and is a co-author of VITP (Vehicular Information Transfer Protocol). More information can be found at http://www.cs.rutgers.edu/~iftode/. Shivkumar Kalyanaram is a professor in the Department of Electrical and Communications Systems Engineering at Rensselaer Polytechnic Institute. His research interests are in the area of computer networking, concentrated around the theme of traffic management and high performance wireless networking. In particular, he works on topics of congestion control, reliability, connectionless traffic engineering, quality of service (QoS), last-mile community wireless networks, low-cost free-space-optical networks, automated network management using online simulation, multicast, multimedia networking (including peer-to-peer multimedia systems), and performance analysis. Lars Kulik is a senior lecturer at the Department of Computer Science and Software Engineering at the University of Melbourne. Until June 2004, he was a postdoctoral researcher at the Department of Spatial Information Science and Engineering, University of Maine. In 2002, he completed his PhD at the University of Hamburg. His overall research goal is to develop a theory of spatial information for building pervasive computing systems that anticipate, adapt and respond to the needs of users, and
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About the Contributors
provide services based on the user’s location and context. Specifically, he focuses on efficient algorithms for moving objects, optimization of traffic throughput using sensor-enabled cars in VANETs, information dissemination algorithms in sensor networks, location privacy, and more generally, efficient spatial algorithms in pervasive computing environments. Antonio Lioy holds a MSc degree in electronic engineering (summa cum laude) and a PhD in computer engineering. Currently he is full professor at the Politecnico di Torino where leads the TORSEC research group that investigates information systems security. This group has taken part to several international security projects, the most recent ones being the European projects Positif, Deserec, Open-TC, and Stork. Since September 2007 Prof. Lioy is a member of the PSG (Permanent Stakeholders’ Group) of ENISA (the European Network and Information Security Agency) and often acts as a consultant and reviewer for public and private bodies on several security issues. His current research interests are in the fields of network security, PKI, and policy-based system protection. Xiaobo Long is a PhD student in the Department of Electrical, Computer and System Engineering at Rensselear Polytechnic Institute, Troy, NY, USA. She got her bachelor's degree (2001) and master's degree (2004) both from University of Electronic Science and Technology of China. Currently, she is working with Professor Biplab Sikdar in Networking Lab. Her research is focused on link level characteristics of wireless communications and computer networks, such as link failures detection in computer networks, wireless channel quality prediction, shadow region detection and intrusion detection at PHY layer in wireless networks. She is also interested in automatic detection and decision and pattern recognition. Syed Masud Mahmud is an associate professor of electrical and computer engineering. He has been long recognized for his teaching excellence in the University. He has been instrumental in educational innovation, such as developing and teaching the first distance-learning course for Wayne State University. In 2002 Dr. Mahmud received the University’s Presidential Award in Excellence in Teaching. In 1994 and also in 2001, he was the recipient of the College of Engineering’s Teaching Excellence Award and received a number of Outstanding Faculty Awards from his College. During the last eighteen years, Dr. Mahmud supervised ten research and development projects funded by local industries. These projects involved developing embedded hardware and software, and simulation models for embedded systems. He also served as a Co-PI of an NSF funded project to develop an undergraduate laboratory for teaching Real-Time Embedded System Networking. He has published over 100 peer reviewed technical journal and conference papers. He also published two editions of a book entitled “Lecture Notes On 16- And 32-Bit Microprocessors” through Simon & Schuster Custom Publishing. Nicholas F. Maxemchuk, received the BSEE degree from the City College of New York, and the MSEE and PhD degrees from the University of Pennsylvania. For the past 7 years he has been a professor in the electical engineering department at Columbia University in New York City. He has a dual appointment as a chief researcher in IMDEA Networks in Madrid. Prior to joining Columbia he spent 25 years at Bell Labs and AT&T Labs, as a member of the technical staff, department head, and technical leader. Prior to joining Bell Labs, he spent 8 years at the RCA David Sarnoff Research Center, as a member of the technical staff. He has been the editor-in-chief of the IEEE Journal on Selected Areas in Communications, an editor for the IEEE Transactions on Communications, and the JACM, was on
334
About the Contributors
the founding committee of the IEEE/ACM Transactions on Networking, and served on their steering committee for 11 years. In 2006 he received the Koji Kobayashi Award for his work in computer communications. He has also been awarded the IEEE’s Leonard G. Abraham Prize Paper Award in 1985 and 1987, for his papers on data and voice on CATV networks and the Manhattan Street networks, and the William R. Bennett Prize Paper Award in 1997, for his paper on an anonymous credit card. Margarita G. Mayoral-Baldivia received the MSc degree in telematics at the University of Colima in 1999. Since 1996, she has been working in the networking area and actually she is a research-professor in telecommunications networks and CCNA Cisco Networking Academy instructor. Her main research focuses in security, wired and wireless networks and bandwidth efficiency. Patrice Maziero was born in Toulouse, France, on December 18, 1984. He received the electronic engineer degree from Ecole Nationale d’Ingénieur (ENIB) of Brest, France in 2007. From 2006 to 2007 he worked on VHDL school project and at TE2M on the creation of a magnetic sensor for crack and corrosion detection on metallic surface covered by thick paint layer. Since 2008, he is a research engineer at the Electronic and Telecommunication Laboratory of Rennes, France (IETR). He works on the automotive network architecture for ECU’s Communication project, especially on PLC and wireless intra-car communication medias. He is involves on the French project ANR CIFAER. Lek Heng Ngoh obtained BSc (Honors) in computer systems engineering from University of Kent at Canterbury, UK in 1986 and obtained MSc and PhD degrees in computer science from University of Manchester, UK in 1987 and 1989 respectively. He has organized numerous international networking conferences, and a review board member of several international journals. He is a founding member of the Singapore Advance Research and Education Network (SingAREN) — an advanced Internet Initiative. He is currently a senior research scientist at the Institute for Infocomm Research (I2R), Agency for Science Technology and Research (A*STAR) in Singapore. Dr. Ngoh is also an adjunct associate professor with the Nanyang Technological University in Singapore. His research interests include: multimedia network protocols, advanced internet technology, P2P mobile computing & wireless sensor networks. Fabienne Nouvel received the MSc and PhD degrees in electrical engineering from National Institute of Applied science (INSA), Rennes, France in 1984 and 1994 respectively. From 1984 to 1990, she was an engineer and worked on DAB, networks and embedded application. In 1990, she joined the research laboratory IETR (Institute of Electronics and Telecommunications of Rennes) and worked on automotive application and the optimization of high data rate networks. In 1995, she joined INSA and currently she is professor in the Department of Telecommunications and Electronic Engineering. She is involved in several European and national research projects in the field of hardware implementation on heterogeneous embedded plate-form and power line transmissions. Her research interests lie both in digital implementation and multicarrier modulation, spread spectrum and multi-user communication. Current research focuses on power line for automotive combined with wireless system, implemented on a reconfigurable plate-form. Kotagiri Ramamohanarao (Rao) received PhD from Monash University. He was awarded the Alexander von Humboldt Fellowship in 1983. He has been at the University Melbourne since 1980 and was appointed a professor in computer science in 1989. Rao held several senior positions including
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About the Contributors
Head of Computer Science and Software Engineering, Head of the School of Electrical Engineering and Computer Science at the University of Melbourne and Research Director for the Cooperative Research Centre for Intelligent Decision Systems. He served on the editorial boards of the Computer Journal. At present he is on the editorial boards for Universal Computer Science, the Journal of Knowledge and Information Systems, IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Statistical Analysis and Data Mining and Very Large Data Bases (VLDB) Journal. He was the program Co-Chair for VLDB, PAKDD, DASFAA and DOOD conferences. He is a steering committee member of IEEE ICDM, PAKDD and DASFAA. He received distinguished contribution award for data mining. Rao is a fellow of the Institute of Engineers Australia, a fellow of Australian Academy Technological Sciences and Engineering and a fellow of Australian Academy of Science. Víctor Rangel-Licea received the B.Eng (Hons) degree in computer engineering in the engineering faculty from the National Autonomous University of Mexico (UNAM) in 1996, the MSc in Telematics at from the University of Sheffield, U.K. in 1998, and the PhD in performance analysis and traffic scheduling in cable networks in 2002, from the University of Sheffield. Since 2002, he has been with the School of Engineering, UNAM, where he is currently a Research-Professor in telecommunications networks. His research focuses on fixed, mesh and mobile broadband wireless access networks, QoS over IP, traffic shaping and scheduling. Sara Sandoval-Carrillo graduated from the Technological Institute of Colima with a BE in informatics, received his MS degree in telematics from the University of Colima, in 1998. Since then, she has been with the College of Telematics, at the University of Colima, where she is currently a researchprofessor in information system. Biplab Sikdar received the B. Tech degree in electronics and communication engineering from North Eastern Hill University, Shillong, India, the M. Tech degree in electrical engineering from Indian Institute of Technology, Kanpur and PhD in electrical engineering from Rensselaer Polytechnic Institute, Troy, NY, USA in 1996, 1998 and 2001, respectively. He is currently an associate professor in the Department of Electrical, Computer and Systems Engineering of Rensselaer Polytechnic Institute, Troy, NY, USA. His research interests include computer networking protocols, network security and queueing theory. Dr. Sikdar is a member of IEEE, Eta Kappa Nu and Tau Beta Pi and currently serves as an associate editor of the IEEE Transactions on Communications. Patcharinee Tientrakool is a PhD student at the department of Electrical Engineering, Columbia University, New York, NY. She received the BS degree in telecommunication engineering from King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand in 2000 and the MS degree in electrical engineering from Columbia University in 2006. Currently she is an engineer at CAT Telecom Public Company Limited, Thailand. Her research interests include vehicular networks and mobile ad hoc networks. Satish Ukkusuri is an assistant professor in the Department of Civil and Environmental Engineering at Rensselaer Polytechnic Institute. Professor Ukkusuri’s expertise is in transportation network modeling with applications to planning and operations. Ukkusuri received the SWUTC Robert Herman Award for outstanding research on transportation network modeling and leadership in transportation
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About the Contributors
during the year 2005. He also holds the Blitman Career Development Chair at RPI which facilitates to conduct research on transportation systems. Professor Ukkusuri is research centers around large scale transportation network modeling and its applications to practice. Apart from his active research, he has been involved in various educational and outreach activities. He presently serves as a faculty advisor for ITE with an overall interest in contributing to the community. Ziyuan Wang received her BEng degree in electric and information engineering from Dalian Jiaotong University in 2005. Currently, she is a PhD student at the Department of Computer Science and Software Engineering at the University of Melbourne. She is working on vehicular ad hoc networks (VANETs), in particular, how to optimize traffic throughput when there are conflicting traffic streams. Her research goal is to develop proactive traffic control strategies for sensor-enabled cars to mitigate traffic congestion and reduce fuel consumption in a decentralized manner. Theodore L. Willke received the BS and MS degrees in electrical engineering and computer science from the University of Illinois, Chicago, in 1993 and 1995, respectively, and the MS degree in electrical engineering from the University of Wisconsin, Madison, in 1998. He is currently working toward the EngScD degree in electrical engineering at Columbia University, New York, NY. In 1998, he joined Intel Corporation, DuPont, WA, where he is currently a principal engineer with the digital enterprise group, where he focuses on I/O technologies and standards. His research interests include wireless communication networks, I/O protocols, and computer system architectures. Ghais El Zein received the PhD and Habilitation a Diriger des Recherches (HDR) degrees in telecommunications signal processing and electronics from Rennes 1 University, France, in 1988 and 1998, respectively. From 1985 to 1987, he was a lecturer and he worked on the first communication systems using spread spectrum in different applications: wireless, power line, multiplexing. In 1988, he joined INSA and currently he is professor in the Department of Telecommunications and Electronic Engineering. Since 2001 he is the assistant director of the Institute of Electronics and Telecommunications of Rennes (IETR) which depends on the French National Center for Scientific Research (CNRS). He is a member of Commissions C and F of CNFRS-URSI. He is involved in several European and national research projects in the field of radiowave propagation, combined with mimo applications and their effects on communication systems performance. His research interests lie in spread spectrum modulation, multi-user communication, space time coding.
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Index
A ad hoc networks 109, 113, 114, 117, 118, 119, 121, 122, 123, 124, 129, 132, 135, 136, 141, 142, 160, 164, 163, 178, 120, 178, 137, 178, 179, 180, 237, 263, 280, 281, 282, 305, 306, 310, 312, 313, 314, 315, 317, 318, 322, 323, 326, 327, 329 advanced driving assistance systems (ADAS) 201, 210, 215 advanced vehicle safety systems (AVS) 105 anti-lock braking system (ABS) 12, 13, 17, 18, 19, 24, 25, 39, 76, 97, 224, 242, 306 ATIS (advanced traveler information systems) 162, 163, 164, 179, 325 ATIS, decentralized 163, 164 automated highway systems (AHS) 105, 188, 189, 198, 319 automotive consortia 8
B bottlenecks 72, 75, 180, 181, 186 brake by wire (BBW) systems 13, 17, 19, 22, 24, 39, 45
C car communications 2, 6, 9, 69, 83, 87, 96, 98, 99, 101 CISPR 12 standard 49, 67, 314 CISPR 25 standard 49, 50, 51, 55, 81
co-operative vehicle infrastructure systems (CVIS) 201, 206, 218, 253, 261, 308 communications, car-to-car 6, 9, 96, 98, 99, 100, 101, 103, 104, 159, 265, 266, 279, 306, 307, 309, 317, 320 communications, intra-vehicle (InV) 1, 2, 3, 9, 1, 3, 4 communications, vehicle-to-infrastructure (V2I) 1, 2, 3, 4, 5, 6, 8, 9, 3, 4, 200, 201, 202, 205, 213, 217, 248 communications, vehicle-to-vehicle (V2V) 1, 2, 3, 4, 5, 6, 8, 9, 3, 4, 105, 200, 201, 202, 204, 205, 213, 216, 217, 248 consortia, LIN (Local Interconnect Network) 2, 29, 62, 63, 71, 72, 75, 76, 84, 85, 86, 87, 89, 90, 98, 104, 284, 288, 302, 319, 327 constant headway state 174 controller area network (CAN) 2, 4, 9, 10, 29, 39, 40, 42, 62, 63, 70, 71, 72, 73, 75, 76, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 284, 286, 287, 288, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 307, 308, 309, 311, 317, 320, 323 cooperative adaptive cruise control (CACC) 201 crosstalk 65 cyber-physical systems 139, 140, 141, 159
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Index
D
G
data exchange 1, 2, 3, 5, 9, 123 dedicated short range communication (DSRC) 2, 3, 4, 5, 7, 9, 11, 64, 99, 100, 103, 106, 112, 142, 159, 164, 181, 250, 251, 256, 261, 263, 269, 278, 281, 282, 304, 310, 314, 326, 327, 328, 329 distance routing effect algorithm for mobility (DREAM) 115, 121, 270, 279, 305 drive-by-wire system 12 drive by wire (DBW) 12 drive by wire (DBW) systems 12, 13, 14, 17, 22, 23, 24, 25, 26, 28, 29, 30, 31, 39, 40, 44, 45
generalized prediction (GP) 33, 34, 35, 37, 38, 39 geographic information system (GIS) 116, 220, 270, 273 greedy perimeter stateless routing (GPSR) 115, 116, 121, 124, 129, 132, 134, 135, 270, 280, 316 grid location service (GLS) 115, 121
E electric vehicles (EV) 13 electro-hydraulic brake (EHB) system 13, 14 electro-mechanical brakes (EMB) 13 electromagnetic compatibility (EMC) 48, 49, 50, 53, 54, 56, 57, 60, 61, 62, 67, 61, 64, 79, 83, 87, 311, 54, 62, 65, 67, 66, 67, 64 electromagnetic interference (EMI) 49, 51, 54, 55, 56, 64, 67 electronic control units (ECUs) 1, 2, 1, 4, 29, 43, 69, 70, 72, 75, 82, 87, 91, 93, 95, 96, 100 electronic noise 17, 26, 39, 49, 52, 54, 55, 56, 57, 62, 63, 64, 65, 77, 78, 48, 78, 66, 78, 79, 83, 86, 90, 98, 171, 221, 223, 224, 225, 227, 229, 232, 233, 235, 240, 243, 308, 325 electrostatic discharge (ESD) 52, 63, 64, 54
F fault tolerant communication 43 fault tolerant control 25, 28, 29, 30, 38, 39, 45 FlexRay 2, 10, 39, 40, 41, 42, 43, 44, 63, 64, 67, 73, 75, 88, 91, 98, 103, 287, 292, 296, 299, 302, 311 4-way stop problem 156
H headway state, constant 174 headway state, intermediate 174 headway state, random 174 human-machine interface (HMI) 220 hybrid-electric propulsion 12 hybrid electric vehicles (HEV) 13, 14, 17
I infotainment applications 69 intelligent cruise control 161, 329 intelligent transportation systems (ITS) 2, 3, 4, 5, 7, 8, 10, 11, 105, 121, 142, 159, 163, 179, 202, 206, 209, 262, 310, 311, 314, 324 interference 42, 48, 49, 51, 54, 55, 56, 57, 58, 60, 62, 63, 64, 66, 67, 77, 78, 79, 84, 85, 106, 107, 162, 163, 165, 166, 167, 168, 167, 168, 169, 170, 171, 173, 174, 175, 177, 178, 179, 232, 235, 250, 258, 269, 305, 306, 311, 315, 320, 321, 323 internal combustion (IC) engine 13, 14, 67, 309 intersection decision systems (IDS) 105 Intra-Vehicle (InV) 1, 9
K Kalman filter 220, 221, 224, 225, 226, 227, 228, 229, 230, 231, 235, 236, 240, 245, 326
L Ladars (Laser detection and ranging sensors) 201, 203
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Index
location-based routing algorithm with clusterbased flooding (LORA-CBF) 121, 125, 127, 128, 129, 132, 134, 135 location aided routing (LAR) 115, 121
M
Steer by wire (SBW) 13 steer by wire (SBW) systems 13, 17, 21, 22, 24, 30, 31, 35, 36, 37, 39, 45
T
medium access control (MAC) 5, 81, 105, 106, 108, 109, 110, 109, 130, 132, 269, 273, 306, 111, 112, 113, 118, 110 merging algorithms 182, 190, 191, 192, 194, 195, 196, 191, 194
throttle by wire (TBW) systems 13, 18, 19, 39 time-period vehicular degree (TPVD) 170 traction control system (TCS) 12, 18, 19 traffic flow control 181, 187, 196 traffic light control 276 traffic network simulator (TRANS) 265
N
U
neighborcast 139, 151, 152, 157 nodes, exposed 166, 167 nodes, hidden 110 nodes, mobile 109, 121, 122, 127, 130, 185, 247
unlicensed national information infrastructure (UNII) 105
P particle filter 229 platooning 22, 143, 145, 159, 160, 144, 188, 189, 208, 312, 326 power line communication (PLC) 69, 75, 76, 77, 78, 79, 81, 82, 83, 85, 87, 90
R Radar 185, 197, 201, 309 random waypoint model (RWP) 181, 182, 184 redundancy, analytical 25, 26, 28, 30, 31, 39, 45 redundancy, dynamic 27, 28, 29 redundancy, static 27, 29 road side units (RSU) 202, 208, 247, 255 route planning 116, 140, 142, 265, 275, 277
S sliding mode observer 30, 31, 32, 39, 46, 313 static vehicular degree (SVD) 170, 171, 172, 177
340
V vehicular ad hoc networks (VANETs) 120, 121, 132, 141, 142, 160, 162, 163, 166, 167, 168, 169, 170, 171, 173, 135, 165, 164, 177, 166, 175, 178, 179, 180, 181, 182, 185, 196, 262, 263, 264, 265, 266, 267, 268, 269, 270, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282 VANET applications 264, 265, 266, 268, 273, 278 Vehicle-to-Vehicle (V2V) 1, 9 vehicular ad hoc networks (VANETs) 142, 164, 165, 170, 173, 175, 177, 182, 184, 185, 197, 198, 205, 206, 247, 248, 249, 250, 251, 101, 178, 199, 252, 251, 255, 260, 261, 262, 263, 304, 306, 307, 308, 310, 312, 313, 316, 317, 318, 319, 322, 323, 324, 326, 327, 328, 329 vehicular degree, static (SVD) 170 vehicular degree, time-period (TVD) 170 vehicular networks 1, 2, 3, 9, 91, 98, 105, 109, 100, 110, 111, 112, 115, 116, 118, 141, 248, 249, 263, 266, 274, 275, 281, 288, 290, 292, 296, 301, 310, 322, 323
341
W
X
wireless access in the vehicular environment (WAVE) 2, 5, 11, 103, 104, 105, 106, 107, 112, 118, 142, 159, 164, 310, 314, 328 wireless networks 81, 86, 109, 110, 119, 123, 136, 137, 142, 143, 146, 160, 168, 179, 197, 198, 269, 280, 307, 308, 315, 316, 323, 324, 328 wireless technologies 1, 3, 9, 69, 86, 141, 142
X-by-wire systems 46, 69, 70, 73, 74, 75, 84, 86, 87, 325 X-tainment applications 69
Y yaw stability control (YSC) 12, 18, 19, 24, 25
