Introduction

  • Chapter
  • First Online:
The Intelligent Safety of Automobile

Part of the book series: Key Technologies on New Energy Vehicles ((KTNEV))

  • 241 Accesses

Abstract

Intelligent safety of vehicle (ISV) refers to the whole process of safety assurance for reliable safety performance, controllable operation risk and effective collision protection of intelligent vehicle (IV) employing advanced theoretical approaches and technologies.

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

Access this chapter

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • 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. Wang Shengchang, Li **nyao. Study on the correlation between driver’s action, reaction characteristics and traffic accidents [J]. Journal of **'an Highway University, 1995 (04): 59-65. [in Chinese]

    Google Scholar 

  2. Mao Enrong, Zhou Yiming. The influence of field dependence and speed estimation ability of motor-vehicle drivers on driving safety [J]. Journal of China Agricultural University, 1997 (02): 114-118. [in Chinese]

    Google Scholar 

  3. He Cundao, Tang Zhendong, Zheng Ninghua. Research on the emotional state of truck drivers [J]. Chinese Journal of Ergonomics, 2001 (02): 18–21+70. [in Chinese]

    Google Scholar 

  4. Li **aohua, He Cundao, Peng Chuqiao, Guo Weili. Research on speed estimation of truck drivers [J]. Psychological Science, 1997 (06): 525–529+575–576. [in Chinese]

    Google Scholar 

  5. Guo Zizheng. Theory and method of dangerous state identification of driving behavior [D]. Southwest Jiaotong University, 2009. [in Chinese]

    Google Scholar 

  6. Li Baichuan, Sun Jianhong, **ao Lijun. Research on the establishment of suitability testing standards for professional car drivers in China[J]. Journal of Safety and Environment, 2001 (03): 7-10. [in Chinese]

    Google Scholar 

  7. Brehmer B. Variable errors set a limit to adaptation[J]. Ergonomics, 1990, 33(10-11): 1231-1239.

    Article  Google Scholar 

  8. Brown I D. Drivers’ margins of safety considered as a focus for research on error[J]. Ergonomics, 1990, 33(10-11): 1307-1314.

    Article  Google Scholar 

  9. Raggatt P T F, Morrissey S A. A field study of stress and fatigue in long-distance bus drivers[J]. Behavioral medicine, 1997, 23(3): 122-129.

    Article  Google Scholar 

  10. Zhang Lei, Wang Jianqiang, Yang Furui, Li Keqiang. Factor analysis and fuzzy clustering of driver behavior modes [J]. Journal of Traffic and Transportation Engineering, 2009, 9 (05): 121-126. [in Chinese]

    Google Scholar 

  11. Holman A C, Havârneanu C E. The Romanian version of the multidimensional driving style inventory: Psychometric properties and cultural specificities[J]. Transportation research part F: traffic psychology and behaviour, 2015, 35: 45-59.

    Article  Google Scholar 

  12. Chen S W, Fang C Y, Tien C T. Driving behaviour modelling system based on graph construction[J]. Transportation research part C: emerging technologies, 2013, 26: 314-330.

    Article  Google Scholar 

  13. Johnson D A, Trivedi M M. Driving style recognition using a smartphone as a sensor platform[]//2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2011: 1609–1615.

    Google Scholar 

  14. Hou Hai**g, ** Lisheng, Guan Zhiwei, Du Haixing, Li **gjun. The influence of driving style on driving behavior [J]. China Journal of Highway and Transport, 2018, 31 (04): 18-27. [in Chinese]

    Google Scholar 

  15. Berdoulat E, Vavassori D, Sastre M T M. Driving anger, emotional and instrumental aggressiveness, and impulsiveness in the prediction of aggressive and transgressive driving[J]. Accident Analysis & Prevention, 2013, 50: 758-767.

    Article  Google Scholar 

  16. Muñoz M, Reimer B, Mehler B. Exploring new qualitative methods to support a quantitative analysis of glance behavior[]//Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 2015: 125–132.

    Google Scholar 

  17. Zheng Dongpeng. Research on drivers’ risk perception and influencing factors [D]. Shanghai Jiaotong University, 2013. [in Chinese]

    Google Scholar 

  18. Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles[J]. ROBOMECH journal, 2014, 1(1): 1-14.

    Article  Google Scholar 

  19. Lawitzky A, Althoff D, Passenberg C F, et al. Interactive scene prediction for automotive applications[]//2013 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2013: 1028–1033.

    Google Scholar 

  20. Hillenbrand J, Spieker A M, Kroschel K. A multilevel collision mitigation approach—Its situation assessment, decision making, and performance tradeoffs[J]. IEEE Transactions on intelligent transportation systems, 2006, 7(4): 528-540.

    Article  Google Scholar 

  21. Worrall S, Orchansky D, Masson F, et al. Improving vehicle safety using context based detection of risk[]//13th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2010: 379–385.

    Google Scholar 

  22. Liu A, Pentland A. Towards real-time recognition of driver intentions[]//Proceedings of Conference on Intelligent Transportation Systems. IEEE, 1997: 236–241.

    Google Scholar 

  23. Berndt H, Emmert J, Dietmayer K. Continuous driver intention recognition with hidden markov models[]//2008 11th International IEEE Conference on Intelligent Transportation Systems. IEEE, 2008: 1189–1194.

    Google Scholar 

  24. Kuge N, Yamamura T, Shimoyama O, et al. A driver behavior recognition method based on a driver model framework[J]. SAE transactions, 2000: 469–476.

    Google Scholar 

  25. Hou Hai**g. Research on recognition way of drivers’ intention for changing roads on expressway [D]. Jilin University, 2013. [in Chinese]

    Google Scholar 

  26. Wang Yingfan, Ning Guobao, Yu Zhuo**. Selection of recognition parameters for passenger-vehicle driver's braking intention [J]. Automotive Engineering, 2011, 33 (03): 213–216+230. [in Chinese]

    Google Scholar 

  27. Wang Qingnian, Tang **anzhi, Wang Pengyu, Sun Lei. Hybrid vehicle control strategy based on driving intention recognition [J]. Journal of Jilin University (Engineering Edition), 2012, 42 (04): 789-795. [in Chinese]

    Google Scholar 

  28. Wang Yuhai, Song Jian, Li **ngkun. Unified recognition and real-time algorithm of driver's intention and driving environment [J]. Journal of Mechanical Engineering, 2006 (04): 206-212. [in Chinese]

    Google Scholar 

  29. Wang Jianqiang, Zheng Xunjia, Huang Heye. The principle of least action followed by driver's driving decision-making mechanism [J]. China Journal of Highway and Transport, 2020,33 (04): 155-168. [in Chinese]

    Google Scholar 

  30. Song Jian, Wang Weiwei, Li Liang, et al. Research status and prospect of vehicle safety technology [J] Journal of Automotive Safety and Energy, 2010,1 (2): 98–106. [in Chinese]

    Google Scholar 

  31. FERGUSON S A. The Effectiveness of Electronic Stability Control in Reducing Real-World Crashes: A Literature Review[J]. Journal of Crash Prevention & Injury Control, 2007, 8(4):329-338.

    Google Scholar 

  32. Insurance Institute for Highway Safety (IIHS). Electronic stability control could prevent nearly one-third of all fatal crashes and reduce rollover risk by as much as 80%; effect is found on single- and multiple-vehicle crashes [EB/OL]. News Release (2006–06–13)[2021–07–03]. http://www.iihs.org/news/rss/pr061306.html.

  33. Volkswagen. Active roll-over protection system. Retrieved 28 August 2012. https://www.volkswagen.co.uk/technology/car-safety/crumple-zones.

  34. HAMID A, ZAKIR U; et al. Autonomous emergency braking system with potential field risk assessment for frontal collision mitigation[J]. 2017 IEEE Conference on Systems, Process and Control (ICSPC). 2017:71–76.

    Google Scholar 

  35. JOAN L. Studies: Automated safety systems are preventing car crashes [EB/OL]. AP News.(2017–08–27)[2020–11–23]. https://phys.org/news/2017-08-automated-safety-car.html.

    Google Scholar 

  36. Jianqiang Wang, Chenfei Yu, Shengbo Eben Li*, Likun Wang. A Forward Collision Warning Algorithm With Adaptation to Driver Behaviors[J]. IEEE Transactions On Intelligent Transportation Systems. 2016, 17(4), 1157–1167.

    Google Scholar 

  37. Li Y, Zheng Y, Wang J, et al. Evaluation of Forward Collision Avoidance system using driver's hazard perception[]. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on. IEEE, 2016: 2273–2278.

    Google Scholar 

  38. "New study confirms real-world safety benefits of autonomous emergency braking”. European Transport Safety Council. 11 July 2015. Retrieved 8 June 2019.

    Google Scholar 

  39. Yougang Bian, Jieyun Ding, Manjiang Hu, Qing Xu, Jianqiang Wang, and Keqiang Li. An Advanced Lane-Kee** Assistance System With Switchable Assistance Modes[J]. IEEE Transactions on Intelligent Transportation Systems, (IF=5.744) .2020, VOL: 21 NO: 1 PP. 385–396.

    Google Scholar 

  40. Lowy, Joan. “Studies: Automated safety systems are preventing car crashes”. AP News. Archived from the original on 2017–08–27.

    Google Scholar 

  41. Road vehicles—Functional safety: ISO 26262[S]. Geneva, Switzerland: ISO: 2018.

    Google Scholar 

  42. Road Vehicles—Safety of the Intended Functionality: ISO/DIS 21448: 2021[S]. Geneva, Switzerland: ISO: 2021.

    Google Scholar 

  43. Cybersecurity Guidebook for Cyber-Physical Vehicle Systems: SAE J3061[S].Geneva:ISO,2016.

    Google Scholar 

  44. Road Vehicles-Cybersecurity Engineering: ISO/SAE DIS 21434[S]. Geneva:ISO,2020.

    Google Scholar 

  45. Zhong Zaimin, Huang **, Zhang Hongbin. Implementation of point to point secure communication based on AUTOSAR [J]. Computer Measurement and Control, 2017, 25 (10): 5. [in Chinese]

    Google Scholar 

  46. B Döbel. Operating system support for redundant multithreading[J]. 2012.

    Google Scholar 

  47. Gao Yi. Research on optimal dispatching of motor vehicle operation safety monitoring system [D]. South China University of Technology, 2012. [in Chinese]

    Google Scholar 

  48. Zheng S , Song Y , Leung T , et al. Improving the Robustness of Deep Neural Networks via Stability Training[J]. Computer Vision & Pattern Recognition, 2016:4480–4488.

    Google Scholar 

  49. Santos F , Draghetti L , Weigel L , et al. Evaluation and Mitigation of Soft-Errors in Neural Network-Based Object Detection in Three GPU Architectures[]// 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2017.

    Google Scholar 

  50. Liu Nan. Design and prototype development of identity authentication mechanism in cybersecurity of intelligent Internet of vehicles [D]. Bei**g University of Posts and Telecommunications, 2020. DOI: https://doi.org/10.26969/d.cnki.gbydu.2020.001589. [in Chinese]

    Article  Google Scholar 

  51. 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 

  52. 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 

  53. LI S, LI K, RAJAMANI R, et al. Model predictive multi-objective vehicular adaptive cruise control[J]. IEEE Transactions on control systems technology, 2010, 19(3): 556-566.

    Article  Google Scholar 

  54. SCHWARTING W, ALONSO-MORA J, RUS D. Planning and decision-making for autonomous vehicles[J]. Annual Review of Control, Robotics, and Autonomous Systems, 2018:357–366.

    Google Scholar 

  55. Keqiang Li, **ao Wang, Youchun Xu, Jianqiang Wang*. Lane changing intention recognition based on speech recognition models[J]. Transportation Research Part C: Emerging Technologies, 2016, 69: 497–514.

    Google Scholar 

  56. **e, G., Gao, H., Qian, L., Huang, B., Li, K., & Wang, J*. Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5999–6008.

    Google Scholar 

  57. 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.

    Article  Google Scholar 

  58. Zheng Yang. Dynamics modeling and distributed control of vehicle platoon based on four-element architecture [D]. Bei**g: Tsinghua University, 2015. [in Chinese]

    Google Scholar 

  59. Jianqiang Wang, Shengbo Eben Li*, Yang Zheng and **aoyun Lu. Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control [J]. Integrated Computer-Aided Engineering, 2015:171–185.

    Google Scholar 

  60. Wang Jianqiang, Wang Haipeng, Liu Jiaxi, et al. Vehicle driving assistance system at intersections based on vehicle-road integration [J]. Journal of China Highway Engineering, 2013, 26 (4): 169-175. [in Chinese]

    Google Scholar 

  61. Editorial Department of China Journal of Highway and Transport. Summary of China's academic research on vehicle engineering. 2017 [J]. China Journal of Highway and Transport, 2017,30 (06): 1–197. [in Chinese]

    Google Scholar 

  62. Chen Ningchuan. Design and control of thermal management system for vehicle power battery pack [D] Bei**g: Tsinghua University, 2016. [in Chinese]

    Google Scholar 

  63. Chen Junyi, Feng Tianyue, Liu Lihao, et al. Automatic generation of specific scenarios for decision-planning system testing [J]. Automobile Technology, 2020 (10): 45-50. [in Chinese]

    Google Scholar 

  64. HALLERBACH S,XIA Y,EBERLE U,et al.Simulation-based identification of critical scenarios for cooperative and automated vehicles[J].SAE International Journal of Connected and Automated Vehicles,2018,1(2018–01–1066): 93–106.

    Google Scholar 

  65. Li Lin, Zhu **chan, Dong **aofei, et al. Research on collision avoidance strategy of autonomous emergency braking system [J] Automotive Engineering, 2015, 37 (2): 168–174. [in Chinese]

    Google Scholar 

  66. Zhu B, Yan S, Zhao J, et al. Personalized lane-change assistance system with driver behavior identification[J]. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10293-10306.

    Article  Google Scholar 

  67. Sundaravadivelu K, Shantharam G, Prabaharan P, et al. Analysis of vehicle dynamics using co-simulation of AVL-CRUISE and CarMaker in ETAS RT environment[]//2014 International Conference on Advances in Electrical Engineering (ICAEE). IEEE, 2014: 1–4.

    Google Scholar 

  68. Huang H, Pan M, Lu Z. Hardware-in-the-loop simulation technology of wide-band radar targets based on scattering center model[J]. Chinese Journal of Aeronautics, 2015, 28(5): 1476-1484.

    Article  Google Scholar 

  69. Tettamanti T, Szalai M, Vass S, et al. Vehicle-in-the-loop test environment for autonomous driving with microscopic traffic simulation[]//2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES). IEEE, 2018: 1–6.

    Google Scholar 

  70. Xu Z, Wang M, Zhang F, et al. PaTAVTT: A hardware-in-the-loop scaled platform for testing autonomous vehicle trajectory tracking[J]. Journal of Advanced Transportation, 2017, 2017.

    Google Scholar 

  71. Li **aochi, Zhao **angmo, Xu Zhigang, et al. Modular flexible testing ground for intelligent connected transport system [J]. China Journal of Highway and Transport, 2019, 32 (06): 137-146. [in Chinese]

    Google Scholar 

  72. Wang J, Shao Y, Ge Y, et al. A survey of vehicle to everything (V2X) testing[J]. Sensors, 2019, 19(2): 334.

    Article  Google Scholar 

  73. Sun Yang, **ong Guangming, Chen Huiyan. Evaluation of unmanned vehicle’s intelligent behavior based on Fuzzy-EAHP [J]. Automotive Engineering, 2014, 36 (01): 22-27. [in Chinese]

    Google Scholar 

  74. Guo Zhangyong. Research on evaluation of driver acceptance of adaptive cruise control system [D]. Changchun: Jilin University, 2014. [in Chinese]

    Google Scholar 

  75. **ong Guangming, Gao Li, Wu Shaobin, et al. Intelligent behavior of driverless vehicles and its testing and evaluation [M] Bei**g: Bei**g University of Technology Press, 2015. [in Chinese]

    Google Scholar 

  76. Yu Rongjie, Tian Ye, Sun Jian. Virtual testing of high-level automated driving vehicle: research progress and frontier [J]. China Journal of Highway and Transport, 2020, 33 (11): 125-138. [in Chinese]

    Google Scholar 

  77. Editorial Department of China Journal of Highway and Transport. Summary of academic research on China's road engineering. 2013 [J]. China Journal of Highway and Transport, 2013, 26 (03): 1–36. [in Chinese]

    Google Scholar 

  78. Glennon J C, Neuman T R, Leisch J E. SAFETY AND OPERATIONAL CONSIDERATIONS FOR DESIGN OF RURAL HIGHWAY CURVES. FINAL REPORT[R]. 1983.

    Google Scholar 

  79. Vogt A, Bared J. Accident models for two-lane rural segments and intersections[J]. Transportation Research Record, 1998, 1635(1): 18-29.

    Article  Google Scholar 

  80. Guo Zhongyin, Yang Yang, Cao Jiwei, et al. Safety evaluation model based on comprehensive index of expressway alignment [J] Journal of Tongji University: Natural Science Edition, 2009 (11): 1472–1476. [in Chinese]

    Google Scholar 

  81. Shi Yang, Chen Yongsheng. Safety evaluation for road alignment design of downhill sections [J]. Western Transportation Science and Technology, 2008 (5): 43-46. [in Chinese]

    Google Scholar 

  82. Hou Tao. Research on safety evaluation of trunk roads based on running speed [D] [D]. Chongqing Jiaotong University, 2009. [in Chinese]

    Google Scholar 

  83. Pei Yulong, Ma Ji. Analysis on the causes of road traffic accidents and research on prevention countermeasures [J]. China Journal of Highway and Transport, 2003, 16 (4): 77-82. [in Chinese]

    Google Scholar 

  84. Xu Zhongyang. Research on safety inspection system of highway subgrade and pavement design [D]. Chang'an University, 2006. [in Chinese]

    Google Scholar 

  85. Guo Zhongyin, Fang Shou'en, et al. Road safety engineering [M]. Bei**g: People's Communications Press, September 2003. [in Chinese]

    Google Scholar 

  86. Zhi Fan, Guo Runhua, Liu Yingqiang. Research on the properties of self-luminous pavement materials based on polymer concrete [J]. Thermosetting Resin, 2022,37 (01): 38-43. DOI: https://doi.org/10.13650/j.cnki.rgxsz.2022.01.017. [in Chinese]

    Article  Google Scholar 

  87. Liu Hanlong, Fei Kang, Ma **aohui, Gao Yufeng. Vibratory die sinking technology for large-diameter cast-in-situ thin-walled pipe piles and its application (I): Development and design [J]. Geotechnical Mechanics, 2003 (02): 164-168. DOI: https://doi.org/10.16285/j.rsm.2003.02.005. [in Chinese]

  88. Chen **feng. Research on traffic guidance and control of long tunnel [D]. Chang'an University, 2002. [in Chinese]

    Google Scholar 

  89. Shan Yongxin. Research on traffic guidance and control strategy of highway tunnel [D]. Chang'an University, 2004. [in Chinese]

    Google Scholar 

  90. Han Zhi, Peng Jianguo, Zheng Hao. Research on anomaly classification and early warning technology of Xuefeng Moutain super long highway tunnel [J]. Journal of Central South Highway Engineering, 2006 (01): 76–78+119. [in Chinese]

    Google Scholar 

  91. Yi Wei. Research on safety evaluation of expressway road safety facilities [D]. South China University of Technology, 2012. [in Chinese]

    Google Scholar 

  92. Liu Z, Wang X, Wang J, et al. Pedestrian movement intention identification model in mixed pedestrian-bicycle sections based on phase-field coupling theory[J]. Advances in Mechanical Engineering, 2018, 10(2): 1687814017746515.

    Article  Google Scholar 

  93. Xu Q, Wu H, Wang J, et al. Roadside pedestrian motion prediction using Bayesian methods and particle filter[J]. IET Intelligent Transport Systems, 2021, 15(9): 1167-1182.

    Article  Google Scholar 

  94. Yang Li, **ao-Yun Lu, Jianqiang Wang*, Keqiang Li. Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning[J]. IEEE transactions on intelligent vehicles, 2020,5 (3):461–474. EI:20200308051536.

    Google Scholar 

  95. Zhang Changlong, Bao Haixing, Du **antong, **e Pengcheng. Application and future of roadside sensing in ICVs [J]. Artificial Intelligence, 2019 (01): 58-66. DOI: https://doi.org/10.16453/j.cnki.issn2096-5036.2019.01.007. [in Chinese]

  96. Wang **ao**g. Intelligent transportation and road traffic safety—Development trends and suggestions []//Proceedings of the Fourth China Intelligent Transportation Annual Conference 2008: 21–32. [in Chinese]

    Google Scholar 

  97. Makino H. Smartway project[J]. Development, 2005.

    Google Scholar 

  98. Chen Chao, Lv Zhiyong, Fu Shanshan, Peng Qi. Overview of the development of vehicle- road collaborative system at home and abroad [J]. Journal of Transport Information and Safety, 2011, 29 (01): 102–105+109. [in Chinese]

    Google Scholar 

  99. Van Arem B, Van Driel C J G, Visser R. The impact of cooperative adaptive cruise control on traffic-flow characteristics[J]. IEEE Transactions on intelligent transportation systems, 2006, 7(4): 429-436.

    Article  Google Scholar 

  100. Naus G J L, Vugts R P A, Ploeg J, et al. String-stable CACC design and experimental validation: A frequency-domain approach[J]. IEEE Transactions on vehicular technology, 2010, 59(9): 4268-4279.

    Article  Google Scholar 

  101. Zheng Y, Li S E, Wang J, et al. Stability and scalability of homogeneous vehicular platoon: Study on the influence of information flow topologies[J]. IEEE Transactions on intelligent transportation systems, 2015, 17(1): 14-26.

    Article  Google Scholar 

  102. Li K, Bian Y, Li S E, et al. Distributed model predictive control of multi-vehicle systems with switching communication topologies[J]. Transportation Research Part C: Emerging Technologies, 2020, 118: 102717.

    Article  Google Scholar 

  103. Wang Jianqiang, Wang Haipeng, Liu Jiaxi, Li Keqiang. Vehicle driving assistance system at intersections based on vehicle-road integration [J]. China Journal of Highway and Transport, 2013, 26 (04): 169–175+183. [in Chinese]

    Google Scholar 

  104. 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.

    Article  Google Scholar 

  105. Wang J, Huang H, Li Y, et al. Driving risk assessment based on naturalistic driving study and driver attitude questionnaire analysis[J]. Accident Analysis & Prevention, 2020, 145: 105680.

    Article  Google Scholar 

  106. 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 

  107. 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.

    Article  Google Scholar 

  108. Jia **ngli, Yang Hongzhi, Liu Chen. Vehicle steady state safety simulation in DVE coupling environment [J]. China Safety Science Journal , 2015, 25 (01): 40-45. [in Chinese]

    Google Scholar 

  109. Chen **aolei. Safety speed assistance system for curve based on DVE cooperation [D]. China Agricultural University, 2014. [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). Introduction. In: The Intelligent Safety of Automobile. Key Technologies on New Energy Vehicles. Springer, Singapore. https://doi.org/10.1007/978-981-99-6399-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6399-7_1

  • 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