基于典型事故场景的雪天高速换道自动驾驶策略

doi:10.13229/j.cnki.jdxbgxb20210267

Abstract

Abstract:

In order to improve the safety of vehicle lane changing on highway in snowy weather， an environment adaptive automatic driving strategy for high-speed safe lane changing of intelligent vehicle was proposed. Based on the typical case of high-speed lane changing traffic accidents， the dynamic mechanism of vehicle instability caused by environment was analyzed， and the lane changing trajectory model was constructed by using Gaussian distribution. Then taking the space-time situation and stability of safe lane changing as constraints， by using decision tree and hierarchical weighted score， a method of cooperative safe lane changing decision and path planning for different driving modes was proposed. Based on the hardware in the loop simulation test platform of intelligent vehicle built by Prescan/Simulink， the traffic accident scene of high-speed lane change of snowy vehicles was used to verify the automatic driving strategy of vehicles on the low attachment road. The simulation results show that the proposed safe lane change decision and path planning mechanism， with vehicle road cooperation， can better ensure the safety and the environmental adaptability of intelligent vehicles， which can provide a reference for the development of intelligent driving decision system.

Key words: engineering communication and transportation safety, automatic driving strategy, snow weather, high-speed lane changing, decision planning, vehicle road cooperation

CLC Number:

U469.2

Tao PENG,Rui FANG,Xing-liang LIU,Hai-wei WANG,Yan-wei PANG,Hong-guo XU,Fu-ju LIU,Tao WANG. Automatic driving strategy of high⁃speed lane changing in snowy weather based on typical accident scenarios[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2558-2567.

Figures/Tables 13

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Table 1

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Table 4

Path planning modes for autonomous vehicle obstacle avoidance of high-speed lane change in snowy"

模式	应用条件	转向频率/Hz/ 制动减速度/（m·s^-2）	分数	优化驾驶策略转向频率/（Hz）/ 制动减速度/（m·s^-2）
1	$S X 0 ≥ 200$	0.10/0.5	6.9	0.10/0.5
		0.15/0	6.8
		0.15/0.5	6.1
		0.20/0	6.0
		0.20/0.5	4.9
		0.10/1	4.1
		0.25/0	4.1
		0.15/1	3.6
		0.20/1	3.1
		0.25/0.5	3.35
		0.10/0	0
2	$170 ≤ S X 0 < 200$	0.15/0	6.8	0.15/0
		0.15/0.5	6.1
		0.20/0	6.0
		0.20/0.5	4.9
		0.25/0	4.1
		0.15/1	3.6
		0.20/1	3.1
		0.25/0.5	3.35
		0.10/0	0
		0.10/0.5	0
		0.10/1	0
3	$140 ≤ S X 0 < 170$	0.20/0	6.0	0.20/0
		0.20/0.5	4.9
		0.25/0	4.1
		0.20/1	3.1
		0.25/0.5	3.35
		0.10/0	0
		0.10/0.5	0
		0.10/1	0
		0.15/0	0
		0.15/0.5	0
		0.15/1	0
4	$108 ≤ S X 0 < 140$	0.25/0	4.1	0.25/0
		0.25/0.5	3.35
		0.10/0	0
		0.10/0.5	0
		0.10/1	0
		0.15/0	0
		0.15/0.5	0
		0.15/1	0
		0.20/0	0
		0.20/0.5	0
		0.20/1	0
5	$S X 0 < 108$	危险

Table 4

Table 5

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Fig.5

Fig.6

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References 15

1	Jonathan J R, Shirley R, Salissou M, et al. What are the factors that contribute to road accidents? an assessment of law enforcement views, ordinary drivers' opinions, and road accident records[J]. Accident Analysis & Prevention, 2018, 115: 11-24.
2	张荣辉, 游峰, 初鑫男, 等. 车-车协同下无人驾驶车辆的换道汇入控制方法[J]. 中国公路学报, 2018, 31(4): 180-191.
	Zhang Rong-hui, You Feng, Chu Xin-nan, et al. Lane change merging control method for unmanned vehicle under V2V cooperation environment[J]. China Journal of Highway and Transport, 2018, 31(4): 180-191.
3	陈虹, 申忱, 郭洪艳, 等. 面向动态避障的智能汽车滚动时域路径规划[J]. 中国公路学报, 2019, 32(1): 162-172.
	Chen Hong, Shen Chen, Guo Hong-yan, et al. Moving Horizon path planning for intelligent vehicle considering dynamic obstacle avoidance[J]. China Journal of Highway and Transport, 2019, 32(1): 162-172.
4	Gipps P G. A model for the structure of lane-changing decisions[J]. Transportation Research, Part B, 1986, 20(5): 403-414.
5	Ratrout N T, Rahman S M. A comparative analysis of currently used microscopic and macroscopic traffic simulation software[J]. The Arabian Journal for Science and Engineering, 2009, 34(1): 121-133.
6	黄玲, 郭亨聪, 张荣辉, 等. 人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型[J]. 中国公路学报, 2020, 33(7): 156-166.
	Huang Ling, Guo Heng-cong, Zhang Rong-hui, et al. LSTM-based lane-changing behavior model for unmanned vehicle under environment of heterogeneous human-driven and autonomous vehicles[J]. China Journal of Highway and Transport, 2020, 33(7): 156-166.
7	Lecun Y, Cosatto E, Ben J, et al. Dave: autonomous off-road vehicle control using end-to-end learning[R]. Courant Institute, New York University, USA:Technical Report DARPA-IPTO Final Report, 2004.
8	杨顺, 蒋渊德, 吴坚, 等. 基于多类型传感数据的自动驾驶深度强化学习方法[J]. 吉林大学学报: 工学版, 2019, 49(4): 1026-1033.
	Yang Shun, Jiang Yuan-de, Wu Jian, et al. Autonomous driving policy learning based on deep reinforcement learning and muti-type sensor data[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1026-1033.
9	Ngai D C K, Yung N H C. A multiple-goal reinforcement learning method for complexvehicle overtaking maneuvers[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 509-522.
10	Matthew M N, Chris U, Jhon M D, et al. Motion planning for autonomous driving with a conformal spatiotemporal lattice[C]∥IEEE International Conference on Robotics and Automation, Shanghai, China, 2011: 4889-4895.
11	Zhang F, Gonzales J, Li S E, et al. Drift control for cornering maneuver of autonomous vehicles[J]. Mechatronics, 2018, 54: 167-174.
12	Nurbaiti W, Hairi Z, Mohd A A R, et al. Study on potential field based motion planning and control for automated vehicle collision avoidance systems[C]∥IEEE International Conference on Mechatronics, Churchill, Australia, 2017: 208-213.
13	孙浩, 邓伟文, 张索民, 等. 考虑全局最优性的汽车微观动态轨迹规划[J]. 吉林大学学报: 工学版, 2014, 44(4): 918-924.
	Sun Hao, Deng Wei-wen, Zhang Suo-min, et al. Micro vehicle dynamic trajectory plan with global optimality[J]. Journal of Jilin University(Engineering and Technology Edition), 2014, 44(4): 918-924.
14	夏小云, 周育人. 蚁群优化算法的理论研究进展[J]. 智能系统学报, 2016, 11: 9-16.
	Xia Xiao-yun, Zhou Yu-ren. Advances in theoretical research of ant colony optimization[J]. CAAI Transactions on Intelligent Systems, 2016, 11: 9-16.
15	Muller U, Ben J, Cosatto E, et al. Offroad obstacle avoidance through endtoend learning[C]∥Advances in Neural Information Processing Systems, Vancouver, Canada, 2006: 739746.

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车辆	参数	数值
客车	宽度B₀/m	2.54
	质心至前轴距离b/m	2.10
	后悬b'/m	3.28
货车	宽度B₁/m	2.50

工况	初始车速 /（m·s^-1）	弯道半径/m	制动减速度 /（m·s^-2）	概率系数	转向频率/Hz	a_0max /（m·s^-2）	φ_c（t）_max /rad	S_Y_c（t_p）_m /m	t_p/s	V_X₀（t_p） /（m·s^-1）	S_X_0m/m	S_X_1m/m	S_X_2m/m
恒速	27.78	2510	—	2.25	0.10	0.5428	0.0274	2.6670	7.0012	27.78	204.4933	19.7317	18.3818
					0.15	0.7998	0.0396	2.7323	5.0632	27.78	150.6557	17.0378	16.2307
					0.20	1.1234	0.0510	2.7928	4.0960	27.78	123.7869	15.6934	15.1571
					0.25	1.4988	0.0616	2.8490	3.5169	27.78	107.6995	14.8885	14.5143
					0.30	1.9141	0.0715	2.9013	3.0976	27.78	96.0513	14.3057	14.0488
制动	27.78	2510	0.5	2.25	0.10	0.7380	0.0304	2.6828	7.0749	25.08	195.8971	19.0246	18.4636
					0.15	0.9432	0.0425	2.7475	5.1296	25.91	147.2571	17.8946	16.3044
					0.20	1.2297	0.0539	2.8076	4.1588	26.33	122.2756	16.5452	15.2268
					0.25	1.5800	0.0645	2.8637	3.5778	26.58	107.0993	15.7376	14.5819
					0.30	1.9783	0.0744	2.9159	3.1915	26.75	96.9155	15.2007	14.1531
			1	2.25	0.10	1.1378	0.0341	2.7027	7.1059	22.33	185.5829	10.0000	18.6632
					0.15	1.2805	0.0459	2.7655	5.1495	24.00	142.2442	13.5067	16.4427
					0.20	1.5040	0.0571	2.8248	4.1740	24.83	119.2046	14.8546	15.3355
					0.25	1.8018	0.0676	2.8804	3.5904	25.33	104.9660	15.2060	14.6731

层次	性能	f/a	最大加速度/（m·s^-2）	最小安全距离/m	评价参数	分值
1	总体	—	—	—	—	s₁=s₂×（i×s₃+j×s₄）
2	安全	—	a_i ≤a_max （i=0，1，2）	S_Xi ≥S_Xi_m （i=0，1，2）	a_i S_Xi	s₂=1（SXi-SXim>0）
2	安全	—	a_i ≤a_max （i=0，1，2）	S_Xi ≥S_Xi_m （i=0，1，2）	a_i S_Xi	s₂=0（SXi-SXim≤0）
3	舒适	0.10/0	0.5428	204.4933	a_0max	s₃=10
		0.10/0.5	0.7380	195.8971		s₃=9
		0.15/0	0.7998	150.6557		s₃=8
		0.15/0.5	0.9432	147.2571		s₃=7
		0.20/0	1.1234	123.7869		s₃=6
		0.10/1	1.1378	185.5829		s₃=5
		0.20/0.5	1.2297	122.2756		s₃=4
		0.15/1	1.2805	142.2442		s₃=3
		0.25/0	1.4988	107.6995		s₃=2
		0.20/1	1.5040	119.2046		s₃=1
		0.25/0.5	1.5800	107.0993		s₃=0.5
4	效率	0.25/0.5	1.5800	107.0993	S_X_0m	s₄=10
		0.25/0	1.4988	107.6995		s₄=9
		0.20/1	1.5040	119.2046		s₄=8
		0.20/0.5	1.2297	122.2756		s₄=7
		0.20/0	1.1234	123.7869		s₄=6
		0.15/1	1.2805	142.2442		s₄=5
		0.15/0.5	0.9432	147.2571		s₄=4
		0.15/0	0.7998	150.6557		s₄=3
		0.10/1	1.1378	185.5829		s₄=2
		0.10/0.5	0.7380	195.8971		s₄=1
		0.10/0	0.5428	204.4933		s₄=0.5

车辆	决策反应时间/s	（制动/加速延迟时间）/s	侧向运动延迟时间/s	初始速度 /（m·s^-1）	距离/m
自动驾驶汽车	0.5	0.05	0.4	27.78	S_X₀=120 S_X₁=20 S_X₂=20
原车道前车	—			0
目标车道前车	0.5	0.05	—	25
目标车道后车	0.5	0.05	—	30

Automatic driving strategy of high⁃speed lane changing in snowy weather based on typical accident scenarios

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