高速公路混合交通环境下的智能网联汽车换道策略研究

doi:10.19562/j.chinasae.qcgc.2024.05.002

Abstract

Abstract:

In order to promote the application of intelligent connected vehicles， the lane changing strategy of intelligent connected vehicles in mixed traffic environment of expressway is proposed. Firstly， the NaSch cellular automata model is improved and the Markov chain algorithm is used to calculate the road capacity. Secondly， for the target lane， the decision-making model based on vehicle speed guidance and the two-matrix decision-making model based on the game theory is established respectively for the dedicated lane and ordinary lane. Finally， the multi-objective trajectory optimization algorithm is used to optimize the lane change trajectory. The results show that the proposed strategy can improve the lane change efficiency by 6% and 3.38%， respectively， for the target dedicated lane and ordinary lane.

Key words: expressway mixed traffic environment, cellular automata, Markov chain, game theory, trajectory planning

Yongtao Liu,Feiran Sun,Shiquan Yuan,Longxin Gao,Ying Cao,Yisong Chen,Jie Qiao. Research on the Intelligent Connected Vehicle Lane Changing Strategies in Mixed Traffic Environment of Expressway[J].Automotive Engineering, 2024, 46(5): 754-765.

Figures/Tables 20

References 19

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车辆总数/辆	最大通行时长/s	最大通行时长对应的智能网联汽车渗透率/%
3 000	6 070	52
4 000	8 550	52
5 000	10 600	51
6 000	12 150	50
8 000	17 000	48
10 000	21 240	48

驾驶风格	速度收益权重w₁	舒适性收益权重w₂	安全收益权重w₃
谨慎型	0.338 0	0.122 9	0.539 1
普通型	0.374 3	0.296 3	0.329 4
莽撞型	0.625 9	0.109 6	0.264 5

纵向位置/m	纵向速度/（m·s^-1）	纵向加速度/（m·s^-2）
234.64	18	0
横向位置/m	横向速度/（m·s^-1）	横向加速度/（m·s^-2）
110.94	0	0

策略	目标车道后车让行（E）	目标车道后车不让行（F）
目标车辆换道（B）	（0.416，0.019）	（0.042，-0.019）
目标车辆不换道（C）	（-0.041，0.019）	（-0.042，-0.019）

策略	目标车道后车让行（E）	目标车道后车不让行（F）
目标车辆换道（B）	（-0.5，1.2）	（0.5，0.98）
目标车辆不换道（C）	（-0.7，-1）	（0.7，-0.8）

Research on the Intelligent Connected Vehicle Lane Changing Strategies in Mixed Traffic Environment of Expressway

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