We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Driving Safety | SpringerLink

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.


Driving Safety

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

  • First Online:
The Intelligent Safety of Automobile

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

  • 263 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 149.79
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 192.59
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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.

    Google Scholar 

  2. LU N, CHENG N, ZHANG N, et al. Connected vehicles: Solutions and challenges [J]. IEEE internet of things journal, 2014, 1(4):289–299.

    Article  Google Scholar 

  3. EL HAJJAJI A, BENTALBA S. Fuzzy path tracking control for automatic steering of vehicles [J]. Robotics and Autonomous systems, 2003, 43(4):203–213.

    Article  Google Scholar 

  4. 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.

    Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. 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.

    Google Scholar 

  7. 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.

    Article  Google Scholar 

  8. Adaptive Integrated Driver-vehicle InterfacE (AIDE) [EB/OL]. [2021-01-19]. http://www.aide-eu.org/objectives.html.

  9. 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.

    Google Scholar 

  10. JACOBSON B. Vehicle Dynamics Compendium for Course MMF062; edition 2016 [R]. Chalmers University of Technology, 2016.

    Google Scholar 

  11. SALVATORE S. The estimation of vehicular speed as a function of visual stimulation [J]. Human factors, 1968, 10(1):27–31.

    Article  Google Scholar 

  12. HORSWILL M S, PLOOY A M. Auditory feedback influences perceived driving speeds [J]. Perception, 2008, 37(7):1037–1043.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. NI D. A unified perspective on traffic flow theory, part I: the field theory [J]. ICCTP 2011: Towards Sustainable Transportation Systems. 2011:4227–4243.

    Google Scholar 

  15. 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.

    Google Scholar 

  16. 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.

    Google Scholar 

  17. STEVENS S S. Psychophysics: Introduction to its perceptual, neural and social prospects [M]. Routledge, 2017.

    Google Scholar 

  18. 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.

    Google Scholar 

  19. BELLER J, HEESEN M, VOLLRATH M. Improving the driver-automation interaction: An approach using automation uncertainty [J]. Human factors, 2013, 55(6):1130–1141.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. 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.

    Google Scholar 

  23. 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.

    Google Scholar 

  24. GUO K, GUAN H. Modelling of driver/vehicle directional control system [J]. Vehicle system dynamics, 1993, 22(3–4):141–184.

    Google Scholar 

  25. MACADAM C C. Understanding and modeling the human driver [J]. Vehicle system dynamics, 2003, 40(1–3):101–134.

    Google Scholar 

  26. 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.

    Google Scholar 

  27. 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.

    Google Scholar 

  28. 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.

  29. 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.

    Google Scholar 

  30. Yu Zhisheng. Vehicle theory [M]. 5th Edition. Bei**g: China Machine Press, 2009 [in Chinese].

    Google Scholar 

  31. WANMING Z. New advance in vehicle-track coupling dynamics [J]. China Railway Science, 2002, 23(2):1–14.

    Google Scholar 

  32. YANG S, CHEN L, LI S. Dynamics of vehicle-road coupled system [M]. London: Springer, 2015.

    Google Scholar 

  33. 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].

    Google Scholar 

  34. 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].

    Google Scholar 

  35. 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.

    Google Scholar 

  36. 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.

    Google Scholar 

  37. 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.

    Google Scholar 

  38. 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.

    Google Scholar 

  39. 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.

    Google Scholar 

  40. 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.

    Google Scholar 

  41. 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.

    Google Scholar 

  42. Zhao **anli. Research on evolution mechanism of airport runway safety risk [D]. Wuhan: Wuhan University of Technology, 2017 [in Chinese].

    Google Scholar 

  43. **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].

    Google Scholar 

  44. Huang Fei. Research on evolution mechanism of traffic accidents on urban expressway with ice and snow [D]. Changchun: Jilin University, 2017 [in Chinese].

    Google Scholar 

  45. 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].

    Google Scholar 

  46. Wang Wuhong, Guo Hongwei, Guo Weiwei. Traffic behavior analysis and safety assessment [M]. Bei**g: Bei**g University of Technology Press, 2013 [in Chinese].

    Google Scholar 

  47. 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.

    Google Scholar 

  48. 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.

    Google Scholar 

  49. WILDE G. Does Risk Homeostasis Theory Have Implications for Road Safety? [J]. Education and Debate, 2002, 32(4):1149–1152.

    Google Scholar 

  50. WILDE, G. The theory of risk homeostasis: implications for safety and health. Risk Analysis. 1982, 2:209–225.

    Google Scholar 

  51. 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.

    Google Scholar 

  52. Fuller R. Towards a general theory of driver behaviour. Accident Analysis and Prevention. 2005, 37:461–472.

    Google Scholar 

  53. 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].

    Google Scholar 

  54. VAN DER MOLEN H H, B?TTICHER A M. A hierarchical risk model for traffic participants [J]. Ergonomics. 1988, 31:537–555.

    Google Scholar 

  55. Ren Futian, Liu **aoming. Analysis of road traffic system safety--Road traffic safety [M]. Bei**g: People's Communications Press, 2001 [in Chinese].

    Google Scholar 

  56. 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.

    Google Scholar 

  57. 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.

    Google Scholar 

  58. 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.

    Google Scholar 

  59. 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].

    Google Scholar 

  60. 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.

    Article  Google Scholar 

  61. NI D. A unified perspective on traffic flow theory, part I: The field theory [J]. Appl. Math. Sci, 2013, 7(39): 1929–1946.

    MathSciNet  Google Scholar 

  62. 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].

    Google Scholar 

  63. Zhang Yihua. Analysis on lateral instability mechanism and research on in-loop control strategy of double-trailer train [D]. Jilin University, 2017 [in Chinese].

    Google Scholar 

  64. 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 

  65. **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].

    Google Scholar 

  66. Bobier C G. A phase portrait approach to vehicle stabilization and envelope control [D]. Stanford University, 2012.

    Google Scholar 

  67. 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.

    Article  Google Scholar 

  68. Zheng Sheng. Research on lateral motion control of automated driving vehicle under extreme conditions [D]. Tsinghua University, 2022 [in Chinese].

    Google Scholar 

  69. 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).

    Google Scholar 

  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.

    Google Scholar 

  71. 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].

    Google Scholar 

  72. 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.

    Google Scholar 

  73. TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations [J]. Physical review E, 2000, 62(2):1805.

    Google Scholar 

  74. Zhao **aocong. Research on the flexible switching of human-machine driving control rights under the DVE interaction [D]. Jiangsu University, 2020 [in Chinese].

    Google Scholar 

  75. 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.

  76. 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.

    Google Scholar 

  77. 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].

    Google Scholar 

  78. 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].

    Google Scholar 

  79. Li Fangyuan. Study on causation mechanism and risk behaviors of major and extraordinary traffic accidents [D]. Chang'an University, 2014 [in Chinese].

    Google Scholar 

  80. Ren Futian, Liu **aoming, Rong Jian, et al. Traffic engineering [M]. Bei**g: People’s Communications Press, 2008 [in Chinese].

    Google Scholar 

  81. Sun Yixuan. Research on road traffic accident analysis based on data mining [D]. Bei**g Jiaotong University, 2014 [in Chinese].

    Google Scholar 

  82. Pei Yulong. Road traffic safety [M]. Bei**g: People’s Communications Press, 2004 [in Chinese].

    Google Scholar 

  83. 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.

    Google Scholar 

  84. 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.

    Google Scholar 

  85. 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].

    Google Scholar 

  86. 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.

    Google Scholar 

  87. 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].

    Google Scholar 

  88. 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].

    Google Scholar 

  89. 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].

    Google Scholar 

  90. China National Standardization Administration. 192,314-T-339, Classification of vehicle driving automation [S]. Bei**g: China Standards Press, January 8, 2020 [in Chinese].

    Google Scholar 

  91. 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.

    Google Scholar 

  92. 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.

    Google Scholar 

  93. 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.

    Article  Google Scholar 

  94. **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.

    Google Scholar 

  95. Liniger A, Lygeros J. A noncooperative game approach to autonomous racing [J]. IEEE Transactions on Control Systems Technology, 2019, 28(3): 884–897.

    Article  Google Scholar 

  96. 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.

    Article  MathSciNet  MATH  Google Scholar 

  97. 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.

    Google Scholar 

  98. 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.

    Google Scholar 

  99. 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.

    Google Scholar 

  100. 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.

    Google Scholar 

  101. 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.

    Article  Google Scholar 

  102. Schwarting W, Pierson A, Karaman S, et al. Stochastic Dynamic Games in Belief Space [J]. IEEE Transactions on Robotics, 2021.

    Google Scholar 

  103. A. I. Goldman et al., “Theory of mind,” The Oxford handbook of philosophy of cognitive science, vol. 1, 2012.

    Google Scholar 

  104. 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.

    Google Scholar 

  105. 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.

    Google Scholar 

  106. 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.

  107. ATEV S, MILLER G, PAPANIKOLOPOULOS N P. Clustering of vehicle trajectories [J]. IEEE transactions on intelligent transportation systems, 2010, 11(3):647–657.

    Google Scholar 

  108. 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.

    Google Scholar 

  109. 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.

    Google Scholar 

  110. 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.

    Google Scholar 

  111. 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].

    Google Scholar 

  112. 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.

    Google Scholar 

  113. 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.

    Google Scholar 

  114. 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].

    Google Scholar 

  115. 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.

    Google Scholar 

  116. Lee H, Taheri S. Intelligent tires? A review of tire characterization Reference [J]. IEEE Intelligent Transportation Systems Magazine, 2017, 9(2): 114–135.

    Article  Google Scholar 

  117. 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].

    Google Scholar 

  118. FRANKJ G, CAROLS, MOSLEHA. QRAS-the quantitative risk assessment system [J]. Reliability Engineering and System Safety, 2006, 91:292–304.

    Google Scholar 

  119. Yin **gbo. Quantitative risk assessment in maritime safety management [M]. Shanghai: Shanghai Jiaotong University Press, 2015 [in Chinese].

    Google Scholar 

  120. 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.

    Article  Google Scholar 

  121. 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.

    Google Scholar 

  122. 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.

    Google Scholar 

  123. LEE K, PENG H. Evaluation of automotive forward collision warning and collision avoidance algorithms [J]. Vehicle System Dynamics, 2005, 43(10):735–751.

    Google Scholar 

  124. 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.

    Google Scholar 

  125. THORSSON J, STEINERT O, Neural Networks for Collision Avoidance [M]. Gothenburg: Chalmers Univ. Technol., 2016.

    Google Scholar 

  126. LEVINE S, FINN C, DARRELL T, et al. End-to-End Training of Deep Visuomotor Policies [J]. 2016, 1–40.

    Google Scholar 

  127. SALLAB A E, ABDOU M, PEROT E, et al. Deep Reinforcement Learning framework for Autonomous Driving [J]. Electronic Imaging, 2017, 2017(19):70–76.

    Google Scholar 

  128. 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.

    Google Scholar 

  129. 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.

    Google Scholar 

  130. 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.

    Google Scholar 

  131. 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.

    Google Scholar 

  132. Zheng Xunjia. Driving risk generation mechanism and its quantitative evaluation [D]. Bei**g: Tsinghua University, 2020 [in Chinese].

    Google Scholar 

  133. 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.

    Google Scholar 

  134. Ni D, Leonard J D, Jia C, et al. Vehicle longitudinal control and traffic stream modeling [J]. Transportation Science, 2015, 50(3): 1016–1031.

    Article  Google Scholar 

  135. 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.

    Google Scholar 

  136. Li Yang. Intelligent vehicle decision-making based on pedestrian behavior prediction [D]. Bei**g: Tsinghua University, 2020 [in Chinese].

    Google Scholar 

  137. 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.

    Google Scholar 

  138. **e Guotao. Research on dynamic environment cognition of intelligent vehicle under uncertainty [D]. Anhui: Hefei University of Technology, 2018 [in Chinese].

    Google Scholar 

  139. 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.

    Google Scholar 

  140. 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].

    Google Scholar 

  141. Huang Shan. Decision control for driving behaviors of multi-ICVs based on game theory [D]. Yanshan University [in Chinese].

    Google Scholar 

  142. 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].

  143. 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].

  144. 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].

    Google Scholar 

  145. 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.

    Article  Google Scholar 

  146. Baidu Apollo Developer Center [EB/OL]. https://apollo.auto/devcenter/document_list_cn.html [in Chinese].

  147. 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.

    Article  Google Scholar 

  148. 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.

    Article  Google Scholar 

  149. 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.

  150. 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].

    Article  Google Scholar 

  151. 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.

    Article  Google Scholar 

  152. LIU Y, BUCKNALL R. Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment [J]. Ocean engineering, 2015, 97:126–144.

    Google Scholar 

  153. 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.

    Google Scholar 

  154. 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.

    Google Scholar 

  155. 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.

    Google Scholar 

  156. 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.

    Google Scholar 

  157. 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.

    Google Scholar 

  158. 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.

    Google Scholar 

  159. Xu Cheng. Research on multi-vehicle cooperative collision avoidance algorithm under partial vehicles’ networking [D]. Bei**g: Tsinghua University, 2015 [in Chinese].

    Google Scholar 

  160. 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.

    Google Scholar 

  161. 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.

    Google Scholar 

  162. 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.

    Google Scholar 

  163. Zheng Yang. Vehicle platoon dynamics modeling and distributed control based on four-element architecture [D]. Bei**g: Tsinghua University, 2015 [in Chinese].

    Google Scholar 

  164. Camponogara E, Jia D, Krogh B H, et al. Distributed model predictive control [J]. IEEE control systems magazine, 2002, 22(1):44–52.

    Google Scholar 

  165. 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.

    Google Scholar 

  166. 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.

    Google Scholar 

  167. 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.

    Google Scholar 

  168. 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.

    Article  Google Scholar 

  169. 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.

    Google Scholar 

  170. Yu Zhisheng. Vehicle Theory. 6th edition [M]. China Machine Press, 2019 [in Chinese].

    Google Scholar 

  171. Zhou Zhili, Xu Liyou. Principle and structure of vehicle ABS. 2nd edition [M]. China Machine Press, 2011 [in Chinese].

    Google Scholar 

  172. 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].

    Google Scholar 

  173. Tesla official website [EB/OL]. [2019-05-25]. https://www.tesla.com/autoleading?redirect=no.

  174. 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.

    Google Scholar 

  175. 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.

    Google Scholar 

  176. 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.

    Google Scholar 

  177. 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.

    Google Scholar 

  178. 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.

    Google Scholar 

  179. 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].

    Google Scholar 

  180. 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].

    Google Scholar 

  181. 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].

  182. 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.

    Google Scholar 

  183. DRESNER K, STONE P. A multiagent approach to autonomous intersection management [J]. Journal of artificial intelligence research, 2008, 31:591–656.

    Google Scholar 

  184. Lu Guangquan, Wang Yunpeng, Tian Daxin. Vehicle-vehicle cooperative safety control technology [M]. Bei**g: Science Press, 2014 [in Chinese].

    Google Scholar 

  185. GUMASTE A, SINGHAI R, SAHOO A. Intellicarts: Intelligent car transportation system [C]. Proc. IEEE LANMAN. 2007.

    Google Scholar 

  186. 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.

    Google Scholar 

  187. 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.

    Google Scholar 

  188. 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].

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianqiang Wang .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 China Communications Press Co., Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

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)

Publish with us

Policies and ethics

Navigation