Vehicle Safety Planning Control Method Based on Variable Gauss Safety Field

作     者：Zhu, Zixuan Teng, Chenglong Cai, Yingfeng Chen, Long Lian, Yubo Wang, Hai 

作者机构：Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China BYD Auto Ind Co Ltd Shenzhen 518118 Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China 

出 版 物：《WORLD ELECTRIC VEHICLE JOURNAL》 (World Electr. Veh. J.)

年 卷 期：2022年第13卷第11期

页      面：203页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：National Natural Science Foundation of China [U20A20333, 51875255, U20A20331, 52072160] Six talent peaks project in Jiangsu Province [2018-TD-GDZB-022] Key R&D projects in Jiangsu Province [BE2020083-3, BE2019010-2] 

主　　题：autonomous driving planning algorithm variable Gaussian safety field reinforcement learning policy gradient 

摘      要：The existing intelligent vehicle trajectory-planning methods have limitations in terms of efficiency and safety. To overcome these limitations, this paper proposes an automatic driving trajectory-planning method based on a variable Gaussian safety field. Firstly, the time series bird s-eye view is used as the input state quantity of the network, which improves the effectiveness of the trajectory planning policy network in extracting the features of the surrounding traffic environment. Then, the policy gradient algorithm is used to generate the planned trajectory of the autonomous vehicle, which improves the planning efficiency. The variable Gaussian safety field is used as the reward function of the trajectory planning part and the evaluation index of the control part, which improves the safety of the reinforcement learning vehicle tracking algorithm. The proposed algorithm is verified using the simulator. The obtained results show that the proposed algorithm has excellent trajectory planning ability in the highway scene and can achieve high safety and high precision tracking control.