Abstract
Driving safety of vehicle (DSV) refers to the realization of IV safety decision and control by means of quantitative evaluation and prediction over comprehensive driving risks, for preventing traffic participants inside and outside the vehicle from unacceptable risks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
XIE S, CHEN S, ZHENG N, et al. Modeling Methodology of Driver-Vehicle-Environment System Dynamics in Mixed Driving Situation [C]. 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2020:1984–1991.
LU N, CHENG N, ZHANG N, et al. Connected vehicles: Solutions and challenges [J]. IEEE internet of things journal, 2014, 1(4):289–299.
EL HAJJAJI A, BENTALBA S. Fuzzy path tracking control for automatic steering of vehicles [J]. Robotics and Autonomous systems, 2003, 43(4):203–213.
HORNG W B, CHEN C Y, CHANG Y, et al. Driver fatigue detection based on eye tracking and dynamic template matching [C]. IEEE International Conference on Networking, Sensing and Control, 2004. IEEE, 2004, 1:7–12.
CLANTON J M, BEVLY D M, HODEL A S. A low-cost solution for an integrated multisensor lane departure warning system [J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(1):47–59.
KATSAROS K, KERNCHEN R, DIANATI M, et al. Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform [C]. 2011 7th International Wireless Communications and Mobile Computing Conference. IEEE, 2011:918–923.
LEE J, PARK B. Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment [J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1):81–90.
Adaptive Integrated Driver-vehicle InterfacE (AIDE) [EB/OL]. [2021-01-19]. http://www.aide-eu.org/objectives.html.
CARSTEN O. From driver models to modelling the driver: what do we really need to know about the driver? Modelling driver behaviour in automotive environments [M]. London: Springer, 2007:105–120.
JACOBSON B. Vehicle Dynamics Compendium for Course MMF062; edition 2016 [R]. Chalmers University of Technology, 2016.
SALVATORE S. The estimation of vehicular speed as a function of visual stimulation [J]. Human factors, 1968, 10(1):27–31.
HORSWILL M S, PLOOY A M. Auditory feedback influences perceived driving speeds [J]. Perception, 2008, 37(7):1037–1043.
WANG J, WU J, LI Y. The driving safety field based on driver-vehicle-road interactions [J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4):2203–2214.
NI D. A unified perspective on traffic flow theory, part I: the field theory [J]. ICCTP 2011: Towards Sustainable Transportation Systems. 2011:4227–4243.
HELLIER E, NAWEED A, WALKER G, et al. The influence of auditory feedback on speed choice, violations and comfort in a driving simulation game [J]. Transportation research part F: traffic psychology and behaviour, 2011, 14(6):591–599.
TANAKA Y, KANEYUKI H, TSUJIY T, et al. Mechanical and perceptual analyses of human foot movements in pedal operation [C]. 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2009:1674–1679.
STEVENS S S. Psychophysics: Introduction to its perceptual, neural and social prospects [M]. Routledge, 2017.
NEWBERRY A C, GRIFFIN M J, DOWSON M. Driver perception of steering feel [J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2007, 221(4):405–415.
BELLER J, HEESEN M, VOLLRATH M. Improving the driver-automation interaction: An approach using automation uncertainty [J]. Human factors, 2013, 55(6):1130–1141.
VAN DEN BEUKEL A P, VAN DER VOORT M C, EGER A O. Supporting the changing driver's task: Exploration of interface designs for supervision and intervention in automated driving [J]. Transportation research part F: traffic psychology and behaviour, 2016, 43:279–301.
NGUYEN A T, SENTOUH C, POPIEUL J C, et al. Shared lateral control with on-line adaptation of the automation degree for driver steering assist system: A weighting design approach [C]. 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015:857–862.
LI R, LI S, GAO H, et al. Effects of human adaptation and trust on shared control for driver-automation cooperative driving [R]. SAE Technical Paper, 2017.
JUGADE S C, VICTORINO A C, CHERFAOUI V B. Shared Driving Control between Human and Autonomous Driving System via Conflict resolution using Non-Cooperative Game Theory [C]. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019:2141–2147.
GUO K, GUAN H. Modelling of driver/vehicle directional control system [J]. Vehicle system dynamics, 1993, 22(3–4):141–184.
MACADAM C C. Understanding and modeling the human driver [J]. Vehicle system dynamics, 2003, 40(1–3):101–134.
SADIGH D, SASTRY S, SESHIA S A, et al. Planning for autonomous cars that leverage effects on human actions. Robotics: Science and Systems [J]. Ann Arbor, MI, USA, 2016, 2.
LI X, YING X, CHUAH M C. Grip: Graph-based interaction-aware trajectory prediction [C]. 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019:3960–3966.
LI J, YANG F, TOMIZUKA M, et al. Evolvegraph: Multi-agent trajectory prediction with dynamic relational reasoning [J]. ar**v preprint ar**v:2003.13924, 2020.
ODHAMS A M C, COLE D J. Models of driver speed choice in curves [C]. Proceedings of the 7th International Symposium on Advanced Vehicle Control. Citeseer, 2004.
Yu Zhisheng. Vehicle theory [M]. 5th Edition. Bei**g: China Machine Press, 2009 [in Chinese].
WANMING Z. New advance in vehicle-track coupling dynamics [J]. China Railway Science, 2002, 23(2):1–14.
YANG S, CHEN L, LI S. Dynamics of vehicle-road coupled system [M]. London: Springer, 2015.
Wang Jianqiang, Wu Jian, Li Yang. Concept, principle and modeling of driving risk field based on DVE cooperation [J]. China Journal of Highway and Transport, 2016, 29 (01): 105–114 [in Chinese].
Liu Qiaobin, Liu Ke, Wang Tao, Gao Ming, Yang Lu, Xu Qing, Wang Jianqiang, Li Keqiang. Human-like trajectory planning for IVs based on lateral quantitative balance index [P]. Bei**g: CN113771884A, 2021-12-10 [in Chinese].
LAM L T. Distractions and the risk of car crash injury: The effect of drivers'age [J]. Journal of safety research, 2002, 33(3):411–419.
DAHLEN E R, MARTIN R C, RAGAN K, et al. Driving anger, sensation seeking, impulsiveness, and boredom proneness in the prediction of unsafe driving [J]. Accident analysis and prevention, 2005, 37(2):341–348.
CHARLTION S G, BAAS P H. Road User Interactions: Patterns of Road Use and Perceptions of Driving Risk [C]. Institution of Professional Engineers New Zealand (IPENZ) Transportation Group. Technical Conference Papers 2002.
KNEE C R, NEIGHBORS C, VFETOR N A. Self‐Determination Theory as a Framework for Understanding Road Rage 1 [J]. Journal of Applied Social Psychology, 2001, 31(5):889–904.
GREENE K, KRCMAR M, WALTERS L H, et al. Targeting adolescent risk-taking behaviors: the contributions of egocentrism and sensation-seeking [J]. Journal of adolescence, 2000, 23(4):439–461.
CHARLTON S G, STARKEY N J, PERRONE J A, et al. What's the risk? A comparison of actual and perceived driving risk [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 25:50–64.
Li **aoyu, Xu Nan, Guo Konghui, et al. An adaptive SMC controller for EVs with four IWMs handling and stability enhancement based on a stability index [J]. Vehicle System Dynamics, 2020: 1–24.
Zhao **anli. Research on evolution mechanism of airport runway safety risk [D]. Wuhan: Wuhan University of Technology, 2017 [in Chinese].
**ong **aoxia. Research on evolution model and blocking strategy of road traffic accident chain based on Markov Chain Theory [D]. Zhenjiang: Jiangsu University, 2018 [in Chinese].
Huang Fei. Research on evolution mechanism of traffic accidents on urban expressway with ice and snow [D]. Changchun: Jilin University, 2017 [in Chinese].
Li **aoyu. Research on instability mechanism and handling stability control of distributed-drive electric vehicles under combined working conditions [D]. Jilin University, 2020 [in Chinese].
Wang Wuhong, Guo Hongwei, Guo Weiwei. Traffic behavior analysis and safety assessment [M]. Bei**g: Bei**g University of Technology Press, 2013 [in Chinese].
OU YK, LIU YC, SHIH FY. Risk prediction model for drivers’ in-vehicle activities-Application of task analysis and back-propagation neural network [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013, 18:83–93.
MCCARTT A T, SHABANOVA V I, LEAF W A. Driving experience, crashes and traffic citations of teenage beginning drivers [J]. Accident Analysis and Prevention, 2003, 35(3):311–320.
WILDE G. Does Risk Homeostasis Theory Have Implications for Road Safety? [J]. Education and Debate, 2002, 32(4):1149–1152.
WILDE, G. The theory of risk homeostasis: implications for safety and health. Risk Analysis. 1982, 2:209–225.
SUMMLA H. Risk Control is not Risk Adjustment: the Zero-risk Theory of Driver Behaviour and Its Implications [J]. Ergonomics, 1988, 31(4):491–506.
Fuller R. Towards a general theory of driver behaviour. Accident Analysis and Prevention. 2005, 37:461–472.
Anderson. Cognitive psychology and its implications [M]. 7th Edition. Translated by Qin Yulin, Cheng Yao, et al. Bei**g: People’s Posts and Telecommunications Press, 2012 [in Chinese].
VAN DER MOLEN H H, B?TTICHER A M. A hierarchical risk model for traffic participants [J]. Ergonomics. 1988, 31:537–555.
Ren Futian, Liu **aoming. Analysis of road traffic system safety--Road traffic safety [M]. Bei**g: People's Communications Press, 2001 [in Chinese].
POLLATSEK A, NARAYANAAN V, PRADHAN A, et al. Using Eye Movements to Evaluate a PC-Based Risk Awareness and Perception Training Program on a Driving Simulator [J]. Human Factors: The Journal of the Human Factors and Ergonomics Society, 2006, 48(3):447–464.
MICHON J A. 1985. A Critical View of Driver Behavior Models: What Do We Know, What Should We Do? [M]. EVANS L, SCHWING R C. Human Behavior and Traffic Safety. Boston, SPRINGER, 1985.
MICHON J A. A Critical View of Driver Behavior Models: What Do We Know, What Should We Do? [G]. EVANS L, SCHWING R C. Human Behavior and Traffic Safety. Boston: Springer, 1985: 485–524.
He Ren, Zhao **aocong, Wang Jianqiang. Modeling of driver risk responsiveness under DVE interaction [J]. China Journal of Highway and Transport, 2020, 33 (09): 236–250 [in Chinese].
ZHAO X, HE R, WANG J. How do drivers respond to driving risk during car-following? Risk-response driver model and its application in human-like longitudinal control [J]. Accident Anal. Prev., 2020, 148: 105783.
NI D. A unified perspective on traffic flow theory, part I: The field theory [J]. Appl. Math. Sci, 2013, 7(39): 1929–1946.
Zheng Xunjia, Huang Heye. Driver’s driving decision-making mechanism follows the principle of least action [J]. China Journal of Highway and Transport, 2020, 33 (04): 155–168 [in Chinese].
Zhang Yihua. Analysis on lateral instability mechanism and research on in-loop control strategy of double-trailer train [D]. Jilin University, 2017 [in Chinese].
Yang **, **%2C%20**ong%20Jian.%20Analysis%20on%20lateral%20stability%20and%20instability%20mechanism%20of%20semi-trailer%20train%20%5BJ%5D.%20Automotive%20Engineering%2C%202011%2C%20033%20%28006%29%3A%20486%E2%80%93492%20%5Bin%20Chinese%5D."> Google Scholar
**ong Lu, Qu Tong, Feng Yuan, Deng Luhua. Criteria of vehicle driving stability under extreme working conditions [J]. Journal of Mechanical Engineering, 2015, 51 (10): 103–111 [in Chinese].
Bobier C G. A phase portrait approach to vehicle stabilization and envelope control [D]. Stanford University, 2012.
Goh J Y, Goel T, Christian Gerdes J. Toward automated vehicle control beyond the stability limits: drifting along a general path [J]. Journal of Dynamic Systems, Measurement, and Control, 2020, 142(2): 021004.
Zheng Sheng. Research on lateral motion control of automated driving vehicle under extreme conditions [D]. Tsinghua University, 2022 [in Chinese].
HYDE N C. The development of a method for traffic safety evaluation: The Swedish Traffic Conflicts Technique [J]. Bulletin Lund Institute of Technology, Department, 1987 (70).
MARKKULA G, MADIGAN R, NATHANAEL D, et al. Defining interactions: A conceptual framework for understanding interactive behaviour in human and automated road traffic [J]. Theoretical Issues in Ergonomics Science, 2020, 21(6):728–752.
Hu Yuanzhi, Lv Zhangjie. Longitudinal collision avoidance algorithm and simulation verification of AEB system based on PreScan [J]. Journal of Automotive Safety and Energy, 2017, 8 (02): 136 [in Chinese].
CHAKROBORTY P, KIKUCHI S. Evaluation of the General Motors based car-following models and a proposed fuzzy inference model [J]. Transportation Research Part C: Emerging Technologies, 1999, 7(4):209–235.
TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations [J]. Physical review E, 2000, 62(2):1805.
Zhao **aocong. Research on the flexible switching of human-machine driving control rights under the DVE interaction [D]. Jiangsu University, 2020 [in Chinese].
AMIR E. Waymo's Big Ambitions Slowed by Tech Trouble. [EB/OL]. [2018-08-28]. https://www.theinformation.com/articles/waymos-big-ambitions-slowed-by-tech-trouble.
TRAUTMAN P, KRAUSE A. Unfreezing the robot: Navigation in dense, interacting crowds [C]. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18–22, 2010, Taipei, Taiwan. IEEE, 2010.
Wang Jianqiang, Yang Lu, Cui Mingyang, Huang Heye, Lin Xuewu, Xu Qing. Comprehensive risk assessment ways and devices for vehicle instability and collision under extreme working conditions [P]. Bei**g: CN113370980B, 2021-11-02 [in Chinese].
Li Yibing, Sun Yueting, Xu Chengliang. Analysis of development trend of vehicle safety technology based on traffic accident data [J]. Journal of Automotive Safety and Energy, 2016, 7 (03): 241–253 [in Chinese].
Li Fangyuan. Study on causation mechanism and risk behaviors of major and extraordinary traffic accidents [D]. Chang'an University, 2014 [in Chinese].
Ren Futian, Liu **aoming, Rong Jian, et al. Traffic engineering [M]. Bei**g: People’s Communications Press, 2008 [in Chinese].
Sun Yixuan. Research on road traffic accident analysis based on data mining [D]. Bei**g Jiaotong University, 2014 [in Chinese].
Pei Yulong. Road traffic safety [M]. Bei**g: People’s Communications Press, 2004 [in Chinese].
AUST M L, FAGERLIND H, SAGBERG F. Fatal intersection crashes in Norway: Patterns in contributing factors and data collection challenges [J]. Accident Analysis and Prevention, 2012, 45:782–791.
WANG W, JIANG X, XIA S, et al. Incident tree model and incident tree analysis method for quantified risk assessment: an in-depth accident study in traffic operation [J]. Safety Science, 2010, 48(10):1248–1262.
Xu Hongguo, Zhang Huiyong, Zong Fang. Bayesian network modeling for traffic accident causation analysis [J]. Journal of Jilin University: Engineering Edition, 2011 (S1): 89–94 [in Chinese].
DELEN D, SHARDA R, BESSONOV M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks [J]. Accident Analysis and Prevention, 2006, 38(3), 434–444.
Mou Haibo, Yu Jianning, Liu Linzhong. Modeling and analysis of causes of traffic accidents based on fuzzy Petri nets [J]. Chinese Journal of Safety Science (12): 93 [in Chinese].
Li Shuqing, Peng Weilang, **ao Liying, et al. Research status and trend analysis of road traffic accident occurrence mechanism [J]. Journal of Safety and Environment, 2014, 14 (03): 14–19 [in Chinese].
ICV Sub Technical Committee of National Technical Committee of Auto Standardization. White paper on design operating conditions of automated driving system [R/OL]. (2020-09) http: ‖ catarc.org.cn/2009151518412898.pdf [in Chinese].
China National Standardization Administration. 192,314-T-339, Classification of vehicle driving automation [S]. Bei**g: China Standards Press, January 8, 2020 [in Chinese].
Phillips D J, Wheeler T A, Kochenderfer M J. Generalizable intention prediction of human drivers at intersections [C]// 2017 IEEE intelligent vehicles symposium (IV). IEEE, 2017: 1665–1670.
Rehder E, Wirth F, Lauer M, et al. Pedestrian prediction by planning using deep neural networks [C]// 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 5903–5908.
Agamennoni G, Nieto J I, Nebot E M. Estimation of multivehicle dynamics by considering contextual information [J]. IEEE Transactions on robotics, 2012, 28(4): 855–870.
**n L, Wang P, Chan C Y, et al. Intention-aware long horizon trajectory prediction of surrounding vehicles using dual lstm networks [C]// 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018: 1441–1446.
Liniger A, Lygeros J. A noncooperative game approach to autonomous racing [J]. IEEE Transactions on Control Systems Technology, 2019, 28(3): 884–897.
Schwarting W, Pierson A, Alonso-Mora J, et al. Social behavior for autonomous vehicles [J]. Proceedings of the National Academy of Sciences, 2019, 116(50): 24972–24978.
KOOIJ J F P, SCHNEIDER N, FLOHR F, et al. Context-Based Pedestrian Path Prediction [C]. European Conference on Computer Vision. Springer, Cham, 2014:618–633.
HUANG H, WANG J, FEI C, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction [J]. Science China Information Sciences, 2020, 63(9):1–15.
WU H, WANG L, ZHENG S, et al. Crossing-Road Pedestrian Trajectory Prediction Based on Intention and Behavior Identification [C]. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020:1–6.
Mohamed A, Qian K, Elhoseiny M, et al. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 14424–14432.
Bahram M, Lawitzky A, Friedrichs J, et al. A game-theoretic approach to replanning-aware interactive scene prediction and planning [J]. IEEE Transactions on Vehicular Technology, 2015, 65(6): 3981–3992.
Schwarting W, Pierson A, Karaman S, et al. Stochastic Dynamic Games in Belief Space [J]. IEEE Transactions on Robotics, 2021.
A. I. Goldman et al., “Theory of mind,” The Oxford handbook of philosophy of cognitive science, vol. 1, 2012.
SCHLECHTRIEMENJ, WEDELA, BREUELG, et al. A probabilistic long term prediction approach for highway scenarios [J]. in IEEE Conference on Intelligent Transportation Systems, 2014:732–738.
LI Y, LU X Y, WANG J, et al. Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning [J]. IEEE Transactions on Intelligent Vehicles, 2020, 5(3):461–474.
HU Y, ZHAN W, TOMIZUKA M. Scenario-transferable semantic graph reasoning for interaction-aware probabilistic prediction [J]. ar**v preprint ar**v:2004.03053, 2020.
ATEV S, MILLER G, PAPANIKOLOPOULOS N P. Clustering of vehicle trajectories [J]. IEEE transactions on intelligent transportation systems, 2010, 11(3):647–657.
RIDEL D, DEO N, WOLF D, et al. Scene compliant trajectory forecast with agent-centric spatio-temporal grids [J]. IEEE Robotics and Automation Letters, 2020, 5(2):2816–2823.
LI J, MA H, ZHAN W, et al. Coordination and trajectory prediction for vehicle interactions via bayesian generative modeling [C]. 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019:2496–2503.
Liang M, Yang B, Hu R, et al. Learning lane graph representations for motion forecasting [C]// European Conference on Computer Vision. Springer, Cham, 2020: 541–556.
Gao Bolin, **e Shugang, Gong **feng. Vehicle sideslip angle estimation based on kinematics-dynamics fusion [J]. Journal of Automobile Safety and Energy, 2015, 000 (001): 72–78 [in Chinese].
Li X, Xu N, Li Q, et al. A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches [J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2020, 234(7): 1930–1943.
Li B, Du H, Li W, et al. Non-linear tyre model-based non-singular terminal sliding mode observer for vehicle speed and side-slip angle estimation [J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of automobile engineering, 2019, 233(1): 38–54.
Li **aoyu, Xu Nan, Guo Konghui. Estimation of centroid sideslip angle based on fusion of kinematics and motion geometry [J]. Journal of Mechanical Engineering, 2020, 56 (02): 121–129 [in Chinese].
Lin X, Wang J, Xu Q, et al. Real-Time Estimation of Tire-Road Friction Coefficient Based on Unscented Kalman Filtering [C]// 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE). IEEE, 2020: 376–382.
Lee H, Taheri S. Intelligent tires? A review of tire characterization Reference [J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(2): 114–135.
He Yong. Status and countermeasures of road traffic safety in China [J]. Journal of Highway and Transportation Research and Development, 2003, 20 (1): 119–122 [in Chinese].
FRANKJ G, CAROLS, MOSLEHA. QRAS-the quantitative risk assessment system [J]. Reliability Engineering and System Safety, 2006, 91:292–304.
Yin **gbo. Quantitative risk assessment in maritime safety management [M]. Shanghai: Shanghai Jiaotong University Press, 2015 [in Chinese].
LI Y, LI K, ZHENG Y, et al. Threat Assessment Techniques in Intelligent Vehicles: A Comparative Survey [J]. IEEE Intelligent Transportation Systems Magazine, 2021, 13(4): 71–91.
ARCHIBALD J K, HILL J C, JEPSEN N A, et al. A Satisficing Approach to Aircraft Conflict Resolution [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2008, 38(4):510–521.
ALLEN C, EWING M, KESHMIRI S, et al. Multichannel sense-and-avoid radar for small UAV [C]. 2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC), 2013:6E2-1–6E2-10.
LEE K, PENG H. Evaluation of automotive forward collision warning and collision avoidance algorithms [J]. Vehicle System Dynamics, 2005, 43(10):735–751.
ALTHOFF M, STURSBERG O, BUSS M. Model-Based Probabilistic Collision Detection in Autonomous Driving [J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2):299–310.
THORSSON J, STEINERT O, Neural Networks for Collision Avoidance [M]. Gothenburg: Chalmers Univ. Technol., 2016.
LEVINE S, FINN C, DARRELL T, et al. End-to-End Training of Deep Visuomotor Policies [J]. 2016, 1–40.
SALLAB A E, ABDOU M, PEROT E, et al. Deep Reinforcement Learning framework for Autonomous Driving [J]. Electronic Imaging, 2017, 2017(19):70–76.
GERDES J C, ROSSETTER E J. A Unified Approach to Driver Assistance Systems Based on Artificial Potential Fields [J]. Journal of Dynamic Systems, Measurement, and Control, 2001, 123(3):431.
ROSSETTER E J, GERDES J C. Lyapunov Based Performance Guarantees for the Potential Field Lane-kee** Assistance System [J]. Journal of Dynamic Systems, Measurement, and Control, 2006, 128(3):510.
ZHENG X, WANG J, WANG J. A Novel Road Traffic Risk Modeling Approach Based on the Traffic Safety Field Concept [J]. CICTP 2018:263–274.
HUANG H, ZHENG X, YANG Y, et al. An integrated architecture for intelligence evaluation of automated vehicles [J]. Accident Analysis and Prevention, 2020, 145:105681.
Zheng Xunjia. Driving risk generation mechanism and its quantitative evaluation [D]. Bei**g: Tsinghua University, 2020 [in Chinese].
HUANG H, WANG J, FEI C, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction [J]. SCIENCE CHINA Information Sciences, 2020, 63(9):190203.
Ni D, Leonard J D, Jia C, et al. Vehicle longitudinal control and traffic stream modeling [J]. Transportation Science, 2015, 50(3): 1016–1031.
WANG J, WU J, ZHENG X, et al. Driving safety field theory modeling and its application in pre-collision warning system [J]. Transportation Research Part C: Emerging Technologies, 2016, 72:306–324.
Li Yang. Intelligent vehicle decision-making based on pedestrian behavior prediction [D]. Bei**g: Tsinghua University, 2020 [in Chinese].
XIE G, GAO H, HUANG B, et al. A Driving Behavior Awareness Model based on a Dynamic Bayesian Network and Distributed Genetic Algorithm [J]. International Journal of Computational Intelligence Systems, 2018, 11(1):469.
**e Guotao. Research on dynamic environment cognition of intelligent vehicle under uncertainty [D]. Anhui: Hefei University of Technology, 2018 [in Chinese].
XIE G, ZHANG X, GAO H, et al. Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios [J]. Sustainability, 2017, 9(9):1582.
Wang Ziqiang, Hu **aoguang, Li **aoxiao, et al. Overview of global path planning algorithms for mobile robots [J]. Computer Science, 2021, 48 (10): 11 [in Chinese].
Huang Shan. Decision control for driving behaviors of multi-ICVs based on game theory [D]. Yanshan University [in Chinese].
Ye Mingfei. Path planning of mobile robot based on Voronoi diagram and uncertainty potential field [D]. University of Electronic Science and Technology of China, 2021. https://doi.org/10.27005/d.cnki.gdzku.2021.001074 [in Chinese].
Liu **ang, Ye **aoming, Wang Quanbin, Li Weiguang, Gao Hanlin. Overview of research on local path planning algorithms for unmanned surface vehicles [J]. Chinese Journal Ship Research, 2021, 16 (S1): 1–10. https://doi.org/10.19693/j.issn.1673-3185.02538 [in Chinese].
Wang Ziqiang, Hu **aoguang, Li **aoxiao, Du Zhuoqun. Overview of global path planning algorithms for mobile robots [J]. Computer Science, 2021, 48 (10): 19–29 [in Chinese].
Urmson C, Anhalt J, Bagnell D, et al. Autonomous driving in urban environments: Boss and the Urban Challenge [J]. Journal of Field Robotics, 2008, 25(8):425–466.
Baidu Apollo Developer Center [EB/OL]. https://apollo.auto/devcenter/document_list_cn.html [in Chinese].
Wang H, Huang Y, Khajepour A, et al. Crash mitigation in motion planning for autonomous vehicles [J]. IEEE transactions on intelligent transportation systems, 2019, 20(9): 3313–3323.
Chen S, Jian Z, Huang Y, et al. Autonomous driving: cognitive construction and situation understanding [J]. Science China Information Sciences, 2019, 62(8): 1–27.
Huang Y, Chen Y. Autonomous driving with deep learning: A survey of state-of-art technologies [J]. ar**v preprint ar**v:2006.06091, 2020.
Wang Jianqiang, Zheng Xunjia, Huang Heye. Driver’s driving decision-making mechanism follows the principle of least action [J]. China Journal of Highway and Transport, 2020, 33 (04): 155–168. https://doi.org/10.19721/j.cnki.1001-7372.2020.04.016 [in Chinese].
Zheng X, Huang H, Wang J, et al. Behavioral decision‐making model of the intelligent vehicle based on driving risk assessment [J]. Computer‐Aided Civil and Infrastructure Engineering, 2021, 36(7): 820–837.
LIU Y, BUCKNALL R. Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment [J]. Ocean engineering, 2015, 97:126–144.
LUO Y, YANG G, XU M, et al. Cooperative lane-change maneuver for multiple automated vehicles on a highway [J]. Automotive Innovation, 2019, 2(3):157–168.
XU Q, CAI M, LI K, et al. Coordinated formation control for intelligent and connected vehicles in multiple traffic scenarios [J]. IET Intelligent Transport Systems, 2021, 15(1):159–173.
CAI M, XU Q, LI K, et al. Multi-lane formation assignment and control for connected vehicles [C]. 2019 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2019:1968–1973.
XU B, BAN X J, BIAN Y, et al. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections [J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(4):1390–1403.
BIAN Y, LI S E, REN W, et al. Cooperation of multiple connected vehicles at unsignalized intersections: Distributed observation, optimization, and control [J]. IEEE Transactions on Industrial Electronics, 2019, 67(12):10744–10754.
XU B, LI S E, BIAN Y, et al. Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections [J]. Transportation Research Part C: Emerging Technologies, 2018, 93:322–334.
Xu Cheng. Research on multi-vehicle cooperative collision avoidance algorithm under partial vehicles’ networking [D]. Bei**g: Tsinghua University, 2015 [in Chinese].
Lu X Y, Wang J, Li S E, et al. Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control [J]. Mathematical Problems in Engineering, 2014, 2014.
MERABTI H, BELARBI K, BOUCHEMAL B. Nonlinear predictive control of a mobile robot:a solution using meta-heuristic [J]. Journal of the Chinese Institute of Engineers, 2016, 39(3):282–290.
FELIPE N, WANDERLEY C, RICARDO C, et al. An adaptive dynamic controller for autonomous mobile robot trajectory tracking [J]. Control Engineering Practice, 2008, 16(11):1354–1363.
Zheng Yang. Vehicle platoon dynamics modeling and distributed control based on four-element architecture [D]. Bei**g: Tsinghua University, 2015 [in Chinese].
Camponogara E, Jia D, Krogh B H, et al. Distributed model predictive control [J]. IEEE control systems magazine, 2002, 22(1):44–52.
KERNER B S. Failure of classical traffic flow theories: stochastic highway capacity and automatic driving [J]. Physica A: Statistical Mechanics and its Applications, 2016, 450:700–747.
TALEBPOUR A, MAHMASSANI H S. Influence of connected and autonomous vehicles on traffic flow stability and throughput [J]. Transportation Research Part C: Emerging Technologies, 2016, 71:143–163.
STERN R E, CUI S, DELLE MONACHE M L, et al. Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments [J]. Transportation Research Part C: Emerging Technologies, 2018, 89:205–221.
Chen C, Wang J, Xu Q, et al. Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control [J]. Transportation Research Part C: Emerging Technologies, 2021, 127: 103138.
Klaus Bengler, Klaus Dietmayer, Berthold F?rber, et al. Three Decades of Driver Assistance Systems: Review and Future Perspectives [J]. IEEE Intelligent Transportation Systems Magazine, 2014, 6(4): 6–22.
Yu Zhisheng. Vehicle Theory. 6th edition [M]. China Machine Press, 2019 [in Chinese].
Zhou Zhili, Xu Liyou. Principle and structure of vehicle ABS. 2nd edition [M]. China Machine Press, 2011 [in Chinese].
Cheng Bo, Zhang Guangyuan, Feng Ruijia, et al. Status and development of driver fatigue monitoring technology [C]. 2007 China International Conference on Automotive Safety Technology and the 10th Annual Conference on Automotive Safety Technology of China Society of Automotive Engineers. 2007 [in Chinese].
Tesla official website [EB/OL]. [2019-05-25]. https://www.tesla.com/autoleading?redirect=no.
THRUN S, MONTEMERLO M, PALATUCCI M. Stanley: The Robot That Won the DARPA Grand Challenge [J]. Journal of Field Robotics, 2009, 23(9):661–692.
CREMEAN L B, FOOTE T B, GILLULA J H, et al. Alice: An Information-Rich Autonomous Vehicle for High-Speed Desert Navigation [J]. Journal of Field Robotics, 2006, 23(9):777–810.
URMSON C, ANHALT J, BAGNELL D, et al. Autonomous driving in urban environments: Boss and the urban challenge [J]. Journal of Field Robotics, 2008, 25(8):425–466.
BACHA A, BAUMAN C, FARUQUE R, et al. Odin: Team victortangos entry in the darpa urban challenge [J]. Journal of field Robotics, 2008, 25(8):467–492.
AHMANE M, ABBAS-TURKI A, PERRONNET F, et al. Modeling and controlling an isolated urban intersection based on cooperative vehicles [J]. Transportation Research Part C: Emerging Technologies, 2013, 28:44–62.
Li Keqiang, Li Jiawen, Chang Xueyang, et al. Principle and typical application of cloud control system for ICVs [J]. Journal of Automotive Safety and Energy, 2020, 11 (03): 261–275 [in Chinese].
Li Keqiang, Chang Xueyang, Li Jiawen, et al. Cloud control system of ICVs and its implementation [J]. Automotive Engineering, 2020, 42 (12): 1595–1605 [in Chinese].
China ICV Industry Innovation Alliance. White paper on vehicle-road-loud integrated control system [R/OL]. (2020-09) [2020-09-28]. http://www.caicv.org.cn/index.php/newsInfo?id=279 [in Chinese].
LEE J, PARK B B, MALAKORN K, et al. Sustainability assessments of cooperative vehicle intersection control at an urban corridor [J]. Transportation Research Part C: Emerging Technologies, 2013, 32:193–206.
DRESNER K, STONE P. A multiagent approach to autonomous intersection management [J]. Journal of artificial intelligence research, 2008, 31:591–656.
Lu Guangquan, Wang Yunpeng, Tian Daxin. Vehicle-vehicle cooperative safety control technology [M]. Bei**g: Science Press, 2014 [in Chinese].
GUMASTE A, SINGHAI R, SAHOO A. Intellicarts: Intelligent car transportation system [C]. Proc. IEEE LANMAN. 2007.
CHOI W, SWAROOP D. Assessing the safety benefits due to coordination amongst vehicles during an emergency braking maneuver [C]. Proceedings of the 2001 American Control Conference, IEEE, 2001, 3:2099–2104.
TATCHIKOU R, BISWAS S, DION F. Cooperative vehicle collision avoidance using inter-vehicle packet forwarding [C]. GLOBECOM'05. IEEE Global Telecommunications Conference, 2005. IEEE, 2005, 5(5):2766.
Wang Pangwei, Yu Guizhen, Wang Yunpeng, et al. Vehicle-vehicle cooperative active collision avoidance algorithm based on sliding mode control [J]. Journal of Bei**g University of Aeronautics and Astronautics, 2014, 40 (2): 268–273 [in Chinese].
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2024 China Communications Press Co., Ltd.
About this chapter
Cite this chapter
Wang, J., Nie, B., Wang, H. (2024). Driving Safety. In: The Intelligent Safety of Automobile. Key Technologies on New Energy Vehicles. Springer, Singapore. https://doi.org/10.1007/978-981-99-6399-7_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-6399-7_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6398-0
Online ISBN: 978-981-99-6399-7
eBook Packages: EngineeringEngineering (R0)