交通规则强约束下瞬态行车风险矢量建模方法研究

doi:10.3969/j.issn.1674-8484.2024.05.015

汽车安全与节能学报 ›› 2024, Vol. 15 ›› Issue (5): 774-782.DOI: 10.3969/j.issn.1674-8484.2024.05.015

交通规则强约束下瞬态行车风险矢量建模方法研究

郑讯佳¹(), 蒋骏皓¹, 李会兰², 陈星¹, 刘辉¹, 王建强³, 高建杰⁴^,^*()

1.重庆文理学院智能制造工程学院，重庆 402160，中国
2.重庆城市职业学院信息与智能制造学院，重庆 402160，中国
3.清华大学车辆与运载学院，北京 100084，中国
4.四川警察学院智能警务四川省重点实验室，泸州 646000，中国

收稿日期:2024-05-10 修回日期:2024-06-04 出版日期:2024-10-31 发布日期:2024-11-07
通讯作者: *高建杰（1985—），男（汉），山东，副教授。E-mail：jianjiecq@163.com。
作者简介:郑讯佳（1990—），男（土家族），重庆，副教授。E-mail：xunjia_zheng@cqwu.edu.cn。
基金资助:
四川省科技计划资助项目(2024NSFSC2029);智能绿色车辆与交通全国重点实验室开放基金课题(KFY2412);智能警务四川省重点实验室开放课题(ZNJW2023KFQN002)

Research on transient driving risk vector modeling method under strong constraints of traffic regulations

ZHENG Xunjia¹(), JIANG Junhao¹, LI Huilan², CHEN Xing¹, LIU Hui¹, WANG Jianqiang³, GAO Jianjie⁴^,^*()

1. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
2. Department of Information and Intelligence Engineering, Chongqing City Vocational College, Chongqing 402160, China
3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
4. Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou 646000, China

Received:2024-05-10 Revised:2024-06-04 Online:2024-10-31 Published:2024-11-07

摘要/Abstract

摘要：

为规避或缓解红绿灯路口前车停车让行时遭到失控后车追尾的严重事故，基于前序研究建立的行车风险场力的基本模型，提出了行车风险的矢量场建模方法。设计了无信号灯交叉路口场景，并进行6组不同状态下的行车安全场仿真计算；设计了红绿灯路口前车停车让行时即将遭到失控后车追尾的危险场景，分析了直行、左转、右转和掉头等4种不考虑道路交通规则约束的避险路径，对比分析了12组不同状态下的行车风险场力分布。结果表明：所提模型可以有效辨识行车风险，自车掉头进入另一侧车道的避险方案最佳，且当车速为3 m/s时能将整体风险降低67.41%。

关键词: 自动驾驶, 行车风险, 矢量建模, 行车安全场

Abstract:

To mitigate or alleviate the occurrence of serious accidents where a preceding vehicle stops to yield at a traffic light intersection and was rear-ended by an out-of-control vehicle, a vector field modeling method for vehicle risk was proposed based on the fundamental model of driving risk field force established in the previous studies. An intersection scenario without traffic signals was designed and the safety simulations was conducted under six driving conditions. A dangerous scenario was developed, where a vehicle at a traffic light intersection was at risk of being rear-ended by an out-of-control following vehicle; then, four evasive maneuvers (going straight, turning left, turning right, and making a U-turn) was analyzed without considering road traffic regulations; finally, the force distribution of driving risks across twelve conditions were compared and analyzed. The results show that the proposed model can effectively identify driving risks. The evasive maneuver of the vehicle making a U-turn into the opposite lane is the most optimal, reducing overall risk by 67.41% when the speed is 3 m/s.

Key words: autonomous driving, driving risk, vector modeling, driving safety field

中图分类号:

U461.91

郑讯佳, 蒋骏皓, 李会兰, 陈星, 刘辉, 王建强, 高建杰. 交通规则强约束下瞬态行车风险矢量建模方法研究[J]. 汽车安全与节能学报, 2024, 15(5): 774-782.

ZHENG Xunjia, JIANG Junhao, LI Huilan, CHEN Xing, LIU Hui, WANG Jianqiang, GAO Jianjie. Research on transient driving risk vector modeling method under strong constraints of traffic regulations[J]. Journal of Automotive Safety and Energy, 2024, 15(5): 774-782.

图/表 13

图1 他车j的风险场力分布

图2 交叉路口场景中的车?车交互关系

图2 无信号灯交叉路口场景

图3 行车安全场场力分布(Pi = [-24]，Pj = [-20, -2]，vj = [20,0])

图4 行车安全场场力分布(Pi = [-24] ，Pj = [-20，-2；16，2]，vj = [20,0；-5,0])

图5 红绿灯路口后车失控追尾场景

图6 场景3 示意图

图7 行车风险场场力分布(Pi = [-5]，vi = [0,0])

图8 行车风险场场力分布(直行)

图9 行车风险场场力分布(左转)

图10 行车风险场场力分布(右转)

图11 行车风险场场力分布(掉头)

序号	场景	v_i / (m·s^-1)	F_ji / kN	F_j1i / kN	F_j2i / kN	F_j3i / kN	风险降低率/ %
1	初始	[0,0]	6.411	0.469	0	5.942	—
2	直行	[0,1]	6.499	1.236	0	5.363	-1.37
3		[0,2]	6.892	2.079	0	4.813	-7.50
4		[0,3]	6.823	2.403	0.125	4.293	-6.40
5	左转	[-6/，7/]	5.860	0.984	0	4.875	8.60
6		[-12/，14/]	4.960	1.734	0	3.227	22.63
7		[-18/，21/]	4.511	2.581	0.055	1.874	29.65
8	右转	[2/，3/]	5.953	0.962	0	4.991	7.15
9		[4/，6/]	5.280	1.658	0	3.522	19.21
10		[6/，9/]	4.246	2.057	0	2.188	33.78
11	掉头	[-1,0]	5.149	0.517	0	4.632	19.68
12		[-2,0]	3.263	0.568	0	2.695	49.11
13		[-3,0]	2.089	0.620	0	1.469	67.41

表1 危险场景风险量化仿真结果

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交通规则强约束下瞬态行车风险矢量建模方法研究

Research on transient driving risk vector modeling method under strong constraints of traffic regulations

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