道路交通安全反事实仿真评估技术综述与展望

doi:10.16265/j.cnki.issn1003-3033.2022.07.2643

摘要/Abstract

摘要：

为理清道路交通安全反事实仿真发展脉络,进一步推动反事实仿真技术的应用,从反事实仿真技术基本原理出发,梳理反事实仿真5个阶段,比较事故场景与自然驾驶场景的差异,讨论仿真模型在驾驶人避险行为、路面条件、事故损伤与事故风险结合等方面的不足,归纳、对比仿真过程、评估指标技术要点;并在分析反事实仿真技术适用性的基础上,归纳该技术的发展趋势。结果表明:驾驶员因素的不确定性限制了反事实仿真的应用领域,目前该技术更适于碰撞、避险阶段的交通安全评价与分析。

关键词: 交通安全, 反事实仿真, 安全评估, 仿真模型, 适用性

Abstract:

In order to clarify the development of counterfactual simulation and further promote application of counterfactual simulation technology, five stages of counterfactual simulation were sorted out, the differences between accident scenarios and natural driving scenarios were compared, the shortcomings of simulation models in the aspects of drivers' avoidance behavior, road conditions, accident damage and accident risk combination were discussed, and the simulation process and technical key points of evaluation indicators were summarized and compared based on the basic principle of counterfactual simulation. In addition, the development trend of counterfactual simulation technology was summarized based on the analysis of its applicability. The results show that the uncertainty of drivers limits the application field of counterfactual simulation and this technology is more suitable for evaluation and analysis of traffic safety during collision and hedging stages currently.

Key words: traffic safety, counterfactual simulation, safety assessment, simulation model, applicability

朱彤, 魏雯, 林淼, 张杰, 刘小龙, 魏田正. 道路交通安全反事实仿真评估技术综述与展望[J]. 中国安全科学学报, 2022, 32(7): 128-135.

ZHU Tong, WEI Wen, LIN Miao, ZHANG Jie, LIU Xiaolong, WEI Tianzheng. Review and prospect of counterfactual simulation for road traffic safety[J]. China Safety Science Journal, 2022, 32(7): 128-135.

图/表 2

参考文献 41

[1]	廉宇峰, 张涛焘, 黄嘉南, 等. 汽车纵向主动避撞系统的研究现状与展望[J]. 计算机应用, 2021, 41(增1): 264-271.
	LIAN Yufeng, ZHANG Taotao, HUANG Jianan, et al. Research status and prospect of longitudinal active collision avoidance system for vehicles[J]. Journal of Computer Applications, 2021, 41(Z1): 264-271.
[2]	ZAFIAN T, RYAN A, AGRAWAL R, et al. Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections[J]. Accident Analysis & Prevention, 2021, 156: DOI: 10.1016/j.aap.2021.106141. doi: 10.1016/j.aap.2021.106141
[3]	TEMEL S, VURAN M C, LUNAR M M R, et al. Vehicle-to-barrier communication during real-world vehicle crash tests[J]. Computer Communications, 2018, 127: 172-186. doi: 10.1016/j.comcom.2018.05.009
[4]	KLÖPPEL-GERSDORF M, GRIMM J. Evaluating suitable glosa-algorithms by simulation considering realistic traffic conditions and V2X-communication[J]. Transport and Telecommunication, 2020, 21(4): 303-310.
[5]	雷晨, 尹志勇. 微型面包车碰撞事故驾驶员损伤重建研究[J]. 重庆理工大学学报:自然科学, 2016, 30(2): 71-76.
	LEI Chen, YIN Zhiyong. Study of driver injury based on reconstruction of minibus collisions[J]. Journal of Chongqing University of Technology:Natural Science, 2016, 30(2): 71-76.
[6]	郁家福, 马宗, 刘丰嘉, 等. 多体动力学仿真数据驱动的列车运行可视化[J]. 机械制造与自动化, 2019, 48(4): 84-88.
	YU Jiafu, MA Zong, LIU Fengjia, et al. Train running visualization based on multibody dynamic simulation data[J]. Machine Building & Automation, 2019, 48(4): 84-88.
[7]	郭子慧, 郭伟伟, 谭墍元. 驾驶员接管自动驾驶车辆的眼动特性和行为分析[J]. 中国安全科学学报, 2022, 32(1): 65-71. doi: 10.16265/j.cnki.issn1003-3033.2022.01.009
	GUO Zihui, GUO Weiwei, TAN Jiyuan. Analysis on eye movement characteristics and behaviors of drivers taking over automated vehicles[J]. China Safety Science Journal, 2022, 32(1): 65-71. doi: 10.16265/j.cnki.issn1003-3033.2022.01.009
[8]	王润民, 朱宇, 赵祥模, 等. 自动驾驶测试场景研究进展[J]. 交通运输工程学报, 2021, 21(2): 21-37.
	WANG Runmin, ZHU Yu, ZHAO Xiangmo, et al. Research progress on test scenarios of autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 21-37.
[9]	LI Li, DONNELL E T. Incorporating Bayesian methods into the propensity score matching framework: a no-treatment effect safety analysis[J]. Accident Analysis & Prevention, 2020, 145: DOI: 10.1016/j.aap.2020.105691. doi: 10.1016/j.aap.2020.105691
[10]	BYRNE R M J. Mental models and counterfactual thoughts about what might have been[J]. Trends in Cognitive Sciences, 2002, 6(10): 426-431. doi: 10.1016/S1364-6613(02)01974-5
[11]	肖凌云, 王琰, 巫小波, 等. 车辆事故深度调查概论[M]. 北京: 中国标准出版社, 2019: 1-170.
[12]	BRUNNER H, ZWIPP H. Scientific approach and methodology of a new in-depth-investigation study in Germany so called GIDAS[C]. The 18^th International Technical Conference on the Enhanced Safety of Vehicles (ESV) 2003: 1-10.
[13]	HAMDANE H, SERRE T, ANDERSON R, et al. Accident simulation and reconstruction for enhancing pedestrian safety: issues and challenges[C]. ESAR 2014-6^th International Conference: Expert Symposium on Accident Research, 2014: 1-11.
[14]	SKYVING M. STRADA: road traffic accident and injury data in Sweden[J]. Journal of Local and Global Health Science, 2015, 2015(2):DOI: 10.5339/jlghs.2015.itma.98. doi: 10.5339/jlghs.2015.itma.98
[15]	STORVIK S G, CAMPBELL J Q, WHEELER J B. Asphyxia in motor vehicle crashes: analysis of crash-related variables using national automotive sampling system crashworthiness data system and forensic case studies[J]. The American Journal of Forensic Medicine and Pathology, 2017, 38(2): 145-152. doi: 10.1097/PAF.0000000000000305
[16]	TSUCHIHASHI M, NISHIKAWA S, MIL K, et al. Road traffic accidents and the abbreviated injury scale (ais) in Japan[J]. Accident Analysis & Prevention, 1981, 13(1): 37-42. doi: 10.1016/0001-4575(81)90041-5
[17]	SEGUI-GOMEZ M, LOPEZ-VALDES F J, FRAMPTON R. Real-world performance of vehicle crash test: the case of EuroNCAP[J]. Injury Prevention, 2010, 16(2):101-106. doi: 10.1136/ip.2009.023812
[18]	SHAIKH J, PUTHAN P, FREDRIKSSON R. Comparison of RASSI database and national road accident statistics of India[C]. IRCOBI Conference Proceedings, 2016: 3-4.
[19]	HUELKE D F, COMPTON C P. Offset frontal collisions: a review of the literature and analysis of UMTRI and NASS crash injury data-CDC, AIS and body area injuries[J]. SAE Transactions, 1995, 104: 839-849.
[20]	MORGENSTERN T, PETZOLDT T, KREMS J F, et al. Using European naturalistic driving data to assess secondary task engagement when stopped at a red light[J]. Journal of Safety Research, 2020, 73: 235-243. doi: 10.1016/j.jsr.2020.03.007
[21]	MATTOS G, GRZEBIETA R, WILLIAMSON A, et al. 250 pedestrian-vehicle interactions: early results from the Australian naturalistic driving study (ANDS)[J]. Injury Prevention, 2016, 22(S2): 91-92.
[22]	栾铭湧, 吴立香, 郭鹏, 等. 基于Prescan和Trucksim模型在环测试研究[J]. 重型汽车, 2020(3): 4-5.
[23]	WILLE J M, ZATLOUKAL M. RateEFFECT-Effectiveness evaluation of active safety systems[M]. Hannover: Fachverlag NW at Carl Schuenemann Verlag GmbH, 2013: 1-41.
[24]	朱冰, 李伟男, 赵健, 等. 考虑驾驶人驾驶习性的自适应车道偏离预警策略[J]. 同济大学学报:自然科学版, 2019, 47(增1): 171-177.
	ZHU Bing, LI Weinan, ZHAO Jian, et al. Adaptive lane departure warning strategy considering driver's driving style[J]. Journal of Tongji University :Natural Science, 2019, 47(S1): 171-177.
[25]	MICHON J A. Human behavior and traffic safety.[M]. Boston: Springer Science & Business Media, 1985: 485-524.
[26]	高亚聪, 周晨静, 荣建. 基于行程时间累积分布曲线的微观交通仿真模型参数标定[J]. 公路交通科技, 2021, 38(4): 121-130.
	GAO Yacong, ZHOU Chenjing, RONG Jian. Parameter calibration of microscopic traffic simulation model based on cumulative distribution curve of travel time[J]. Journal of Highway and Transportation Research and Development, 2021, 38(4): 121-130.
[27]	朱顺应, 蒋若曦, 王红, 等. 机动车交通冲突技术研究综述[J]. 中国公路学报, 2020, 33(2): 15-33. doi: 10.19721/j.cnki.1001-7372.2020.02.002
	ZHU Shunying, JIANG Ruoxi, WANG Hong, et al. Review of research on traffic conflict techniques[J]. China Journal of Highway and Transport, 2020, 33(2): 15-33. doi: 10.19721/j.cnki.1001-7372.2020.02.002
[28]	吴斌, 朱西产, 沈剑平. 基于自然驾驶数据的驾驶员紧急转向变道模型[J]. 同济大学学报:自然科学版, 2019, 47(11): 1618-1625.
	WU Bin, ZHU Xichan, SHEN Jianping. Analysis of driver emergency steering lane change behavior model based on naturalistic driving data[J]. Journal of Tongji University :Natural Science, 2019, 47(11): 1618-1625.
[29]	胡宏宇, 刘家瑞, 高菲, 等. 基于一维卷积神经网络的驾驶人身份识别方法[J]. 中国公路学报, 2020, 33(8): 195-203. doi: 10.19721/j.cnki.1001-7372.2020.08.019
	HU Hongyu, LIU Jiarui, GAO Fei, et al. Driver identification based on 1-D convolutional neural networks[J]. China Journal of Highway and Transport, 2020, 33(8): 195-203. doi: 10.19721/j.cnki.1001-7372.2020.08.019
[30]	SEACRIST T, SAHANI R, CHINGAS G, et al. Efficacy of automatic emergency braking among risky drivers using counterfactual simulations from the SHRP 2 naturalistic driving study[J]. Safety Science, 2020, 128: DOI: 10.1016/j.ssci.2020.104746. doi: 10.1016/j.ssci.2020.104746
[31]	陈小复. 美国IIHS的汽车研究中心(上)[J]. 汽车与配件, 2003(22): 30-34.
[32]	BÄRGMAN J, VICTOR T. Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?[J]. IET Intelligent Transport Systems, 2019, 14(9): 1058-1067. doi: 10.1049/iet-its.2018.5550
[33]	AUTEY J, SAYED T, ZAKI M H. Safety evaluation of right-turn smart channels using automated traffic conflict analysis[J]. Accident Analysis & Prevention, 2012, 45: 120-130. doi: 10.1016/j.aap.2011.11.015
[34]	SHELBY S G. Delta-V as a measure of traffic conflict severity[C]. 3^rd International Conference on Road Safety and Simulation, 2011: 14-16.
[35]	唐梅. 不同侧面碰撞角度对驾驶员损伤研究[D]. 成都: 西华大学, 2020.
	TANG Mei. Research on demage to drivers from different side impact angles[D]. Chengdu: Xihua University, 2020.
[36]	BÄRGMAN J, LISOVSKAJA V, VICTOR T, et al. How does glance behavior influence crash and injury risk? a 'what-if' counterfactual simulation using crashes and near-crashes from SHRP2[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2015, 35: 152-169. doi: 10.1016/j.trf.2015.10.011
[37]	SANDER U, LUBBE N, PIETZSCH S. Intersection AEB implementation strategies for left turn across path crashes[J]. Traffic Injury Prevention, 2019, 20(S1): 119-125.
[38]	ZHU Meixin, WANG Xuesong, WANG Yinhai. Human-like autonomous car-following model with deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies, 2018, 97(11): 348-368. doi: 10.1016/j.trc.2018.10.024
[39]	SUN Jian, QI Xiao, XU Yiming, et al. Vehicle turning behavior modeling at conflicting areas of mixed-flow intersections based on deep learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 21(9): 3674-3685. doi: 10.1109/TITS.2019.2931701
[40]	CACCIABUE P C, RICCIOLI C, LUEDTKE A, et al. Human modelling in assisted transportation[M]. Milano: Springer, 2014: 109-371.
[41]	NHTSA. Crash report sampling system: generalized variance functions[R], 2020.