On-ramp merging strategy for connected and automated vehicles based on complete information static game

作     者：Haigen Min Yukun Fang Xia Wu Guoyuan Wu Xiangmo Zhao Haigen Min;Yukun Fang;Xia Wu;Guoyuan Wu;Xiangmo Zhao

作者机构：School of Information EngineeringChang'an UniversityXi'an 710064China Department of Electrical and Computer EngineeringUniversity of CaliforniaRiversideCA 92521USA 

出 版 物：《Journal of Traffic and Transportation Engineering(English Edition)》 (交通运输工程学报（英文版）)

年 卷 期：2021年第8卷第4期

页      面：582-595页

核心收录：

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

基　　金：supported in by National Natural Science Foundation of China (No.61903046) Key Research and Development Program of Shaanxi Province (No.2021GY-290) Youth Talent Lift Project of Shaanxi Association for Science and Technology (No.20200106) Joint Laboratory for Internet of Vehicles,Ministry of Education-China Mobile Communications Corporation (No.213024170015) Fundamental Research Funds for the Central Universities (No. 300102240106) 

主　　题：Connected and automated vehicles On-ramp merging Complete information static game Optimal control Varying-scale grid search 

摘      要：Improper handling of vehicle on-ramp merging may hinder traffic flow and contribute to lower fuel economy,while also increasing the risk of *** control for connected and automated vehicles(CAVs)has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic ***,in this paper,we focus on the scenario of CAVs on-ramp merging and propose a centralized control *** sequence(MS)allocation and motion planning are two key issues in this *** deal with these problems,we first propose an MS allocation method based on a complete information static game whereby the mixed-strategy Nash equilibrium is calculated for an individual vehicle to select its *** on-ramp merging problem is then formulated as a bi-objective(total fuel consumption and total travel time)optimization problem,to which optimal control based on Pontryagin s minimum principle(PMP)is applied to solve the motion planning *** determine the proper parameters in the bi-objective optimization problem,a varying-scale grid search method is proposed to explore possible solutions at different *** this method,an improved quicksort algorithm is designed to search for the Pareto front,and the(approximately)unbiased Pareto solution for the bi-objective optimization problem is finally determined as the optimal *** proposed on-ramp merging strategy is validated via numerical simulation,and comparison with other strategies demonstrates its effectiveness in terms of fuel economy and traffic efficiency.