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 

机构地区：[1]School of Information Engineering,Chang'an University,Xi'an 710064,China Department of Electrical and Computer Engineering,University of California,Riverside,CA 92521,USA

出　　处：《Journal of Traffic and Transportation Engineering(English Edition)》2021年第4期582-595,共14页交通运输工程学报（英文版）

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)

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 collisions.Cooperative control for connected and automated vehicles(CAVs)has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic efficiency.Therefore,in this paper,we focus on the scenario of CAVs on-ramp merging and propose a centralized control method.Merging sequence(MS)allocation and motion planning are two key issues in this process.To 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 strategy.The 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 issue.To determine the proper parameters in the bi-objective optimization problem,a varying-scale grid search method is proposed to explore possible solutions at different scales.In 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 solution.The 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.

关 键 词：Connected and automated vehicles On-ramp merging Complete information static game Optimal control Varying-scale grid search 

分 类 号：U491[交通运输工程—交通运输规划与管理]