面向城市道路的智能网联汽车多车道轨迹优化方法  

Multi-lane Trajectory Optimization for Intelligent Connected Vehicles in Urban Road Network

作　　者：王庞伟[1] 刘程 汪云峰 张名芳 Wang Pangwei;Liu Cheng;Wang Yunfeng;Zhang Mingfang(North China University of Technology,Beijing Key Lab of Urban Intelligent Traffic Control Technology,Beijing 100144;Research Institute of Highway Ministry of Transport,Key Laboratory of Operation Safety Technology on Transport Vehicles,Beijing 100088)

机构地区：[1]北方工业大学,城市道路交通智能控制技术北京市重点实验室,北京100144 [2]交通运输部公路科学研究院,运输车辆运行安全技术交通行业重点实验室,北京100088

出　　处：《汽车工程》2024年第2期241-252,328,共13页Automotive Engineering

基　　金：国家重点研发计划(2022YFB4300400);北京市自然科学基金(4212034)资助。

摘　　要：为提高城市路网下智能网联汽车的通行效率以及燃油效率,提出面向城市道路的多车道时空轨迹优化方法。首先,结合多车道时空位置关系定义智能网联汽车状态与约束,综合考虑通行效率与燃油经济性构建时空轨迹复合优化模型,并采用庞特里亚金极大值算法进行求解。然后,本文设定协同换道的规则,并通过Q-learning算法获取最优的换道策略。最后,通过SUMO/Python联合仿真验证了该方法可以在不同车辆饱和程度、绿信比状态及最低通行速度条件下有效提高通行效率,且燃油效率得到明显改善。In order to improve the traffic efficiency and fuel utilization efficiency of intelligent connected vehicles(ICVs)under urban traffic networks,a multilane spatiotemporal trajectory optimization method is proposed in this paper.Firstly,the state and constraints of the ICVs are defined based on the multi-lane spatiotemporal posi⁃tion relationship and the compound optimization model of spatiotemporal trajectory is constructed by considering the traffic efficiency and fuel economy,which is solved by the Pontryagin Maximum algorithm.Furthermore,the rules of cooperative lane change are designed to obtain the optimal lane change strategy by Q-learning algorithm.Finally,the SUMO/Python co-simulation tests show that the method can effectively improve the traffic efficiency under differ⁃ent vehicle saturation levels,split allocation,and minimum traffic speed conditions,with great improvement of fuel efficiency.

关 键 词：智能网联汽车 多车道轨迹优化 Q-学习 城市交通网络 SUMO/Python联合仿真 

分 类 号：U495[交通运输工程—交通运输规划与管理]