An Evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

作     者：Dong, Changyin Chen, Yujia Wang, Hao Ni, Daiheng Shi, Xiaomeng Lyu, Keyun 

作者机构：Southeast Univ Transportat Planning & Management Nanjing Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing Peoples R China Southeast Univ Dept Transportat Transportat Planning & Management Nanjing Peoples R China Southeast Univ Transportat Engn Sch Transportat Nanjing Peoples R China Southeast Univ Sch Transportat Nanjing Peoples R China Southeast Univ Transportat Engn Nanjing Peoples R China Southeast Univ Jiangsu Key Lab Urban Intelligent Transportat Sys Nanjing Peoples R China Southeast Univ Inst Traff Engn Nanjing Peoples R China Univ Massachusetts Civil Engn Amherst MA USA 

出 版 物：《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 (IEEE Trans. Veh. Technol.)

年 卷 期：2023年第72卷第2期

页      面：1611-1628页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：National Key Research and Development Program of China [2019YFB1600200] National Natural Science Foundation of China [51878161, 52072067, 71901060] Natural Science Foundation of Jiangsu Province [BK20210249] China Postdoctoral Science Foundation [2020M681466] Jiangsu Planned Projects for Postdoctoral Research Funds [SBK2021041144, 2021K094A] 

主　　题：Autonomous vehicle evolutionary algorithms lane-changing model machine learning cooperative adaptive cruise control 

摘      要：This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.