Collaborative decision-making method of high-speed multi-vehicle multi-driving behavior confliction

doi:10.3969/j.issn.1674-8484.2023.05.010

Abstract

Abstract:

A collaborative decision-making method of driving behavior conflict was proposed based on the optimal matching of dichotomous graph to solve the problem of spatial position conflict of multi-vehicle and multi-driving behavior in the highway scenario. A set of feasible candidates driving behaviors of vehicles was created according to the static traffic information. A utility function was constructed according to five evaluation indicators, including the average lane speed, the vehicle density, the travel space, the time to collision (TTC), and the driving burden, to quantitatively evaluate the candidate driving behaviors. Taking the potential spatial position at the end of the vehicle's driving behavior as the target set, and the driving behavior evaluation utility as the weight, a dichotomous graph based on the vehicle set and the target set was constructed. Taking the maximum global total utility value as the decision-making goal, the Kuhn-Munkres (KM) algorithm was used to solve the optimal matching. A simulation scenario is built to verify the effectiveness of the method. The results show that the collaborative decision-making method effectively solve the conflict of multi-vehicle and multi-driving behavior, ensure vehicle driving safety, enhance the utility value by 2% and the average speed by 8% at the initial and final moments of the road, results in increased the traffic efficiency, and the accuracy of driving behavior decision-making is 11% and 9% higher than that of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), respectively. At the same time, the real-time performance of KM algorithm is much higher than that of GA algorithm and PSO algorithm.

Key words: autonomous driving, collaborative decision-making, bipartite graph, Kuhn-Munkres (KM) algorithm, utility

CLC Number:

U461

ZHANG Xinfeng, WU Lin, LI Zhiyuan, LIU Huan. Collaborative decision-making method of high-speed multi-vehicle multi-driving behavior confliction[J]. Journal of Automotive Safety and Energy, 2023, 14(5): 609-617.

Figures/Tables 16

References 15

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Lane 1限速	110~120 km/h
Lane 2限速	90~110 km/h
Lane 3限速	60~100 km/h
观测区域长度	200 m
车道宽度	3.5 m
车长×车宽	6.0 m × 1.8 m
速度评价指标权重， α_v	0.40
车辆密度评价指标权重， α_dens	0.15
行进空间评价指标权重，α_spa	0.20
TTC评价指标权重，α_TTC	0.20
行驶负担评价指标权重， α_dirv	0.05
车辆最小安全距离，2l	12 m
驾驶行为持续时间，t_dura	4.0 s
最大行进空间，S_max	200 m

Lane 1限速	110~120 km/h
Lane 2限速	90~110 km/h
Lane 3限速	60~100 km/h
观测区域长度	200 m
车道宽度	3.5 m
车长×车宽	6.0 m × 1.8 m
速度评价指标权重， α_v	0.40
车辆密度评价指标权重， α_dens	0.15
行进空间评价指标权重，α_spa	0.20
TTC评价指标权重，α_TTC	0.20
行驶负担评价指标权重， α_dirv	0.05
车辆最小安全距离，2l	12 m
驾驶行为持续时间，t_dura	4.0 s
最大行进空间，S_max	200 m

驾驶行为	效用值	潜在位置区间/ m	目标车道编号
(m₁, n₁)	0.773	(116.2,134.2)	1
(m₁, n₂)	0.773	(121.8,139.8)	1
(m₃, n₁)	0.705	(116.2,134.2)	2
(m₃, n₃)	0.705	(108.6,126.6)	2

驾驶行为	效用值	潜在位置区间/ m	目标车道编号
(m₁, n₁)	0.773	(116.2,134.2)	1
(m₁, n₂)	0.773	(121.8,139.8)	1
(m₃, n₁)	0.705	(116.2,134.2)	2
(m₃, n₃)	0.705	(108.6,126.6)	2

目标	潜在位置区间/ m	车道编号
T₁	(121.8,139.8)	1
T₂	(116.2,134.2)	2
T₃	(147.3,165.3)	1
T₄	(120.7,138.7)	2
T₅	(107.9,125.9)	3
T₅	(108.4,126.4)	3
T₆	(174.2,192.2)	1
T₇	(166.2,184.2)	2
T₇	(171.3,189.3)	2
T₈	(169.6,187.6)	3