Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm
In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can int...
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| Vydané v: | Procedia computer science Ročník 262; s. 1166 - 1172 |
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| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
2025
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better. |
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| AbstractList | In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better. |
| Author | Wang, Jia |
| Author_xml | – sequence: 1 givenname: Jia surname: Wang fullname: Wang, Jia email: 15294120786@163.com organization: Lanzhou Institute of Technology, Lanzhou 730050, China |
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| Copyright | 2025 |
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| DOI | 10.1016/j.procs.2025.05.156 |
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| Keywords | DWA algorithm Vehicle trajectory planning Autonomous driving Reinforcement learning algorithm |
| Language | English |
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| References | Bo, Lei, Jie (bib9) 2023; 37 Ban-qiang, Chagen, Zhou-Ping (bib2) 2023; 11 Yue, Pengpeng, Ruyu (bib8) 2023; 41 Jianming, Fengsheng, Di (bib1) 2023; 44 Jingda, Zhiyu, Zhongxu (bib11) 2023; 2 Jianjun, Xiaoyi, Xiaoqiang (bib3) 2024; 68 Xinyang, Zhenbo, Weiqing (bib10) 2024; 41 Ning Qiang, Liu Yuansheng, Xie Longyang. Automatic vehicle control method based on SAC application. Computer engineering and application, 2023, 59 (8): 9. Shi Gaosong, Zhao Qinghai, Dong Xin, et al. Autopilot based on PPO algorithm man-machine interactive reinforcement learning method. Computer application research, 2024 (9):25-29. Bingli, Yafei (bib7) 2023; 46 Yanliang, Baorong, Yuan (bib6) 2024; 37 Ban-qiang (10.1016/j.procs.2025.05.156_bib2) 2023; 11 Yanliang (10.1016/j.procs.2025.05.156_bib6) 2024; 37 Jianjun (10.1016/j.procs.2025.05.156_bib3) 2024; 68 Jingda (10.1016/j.procs.2025.05.156_bib11) 2023; 2 Xinyang (10.1016/j.procs.2025.05.156_bib10) 2024; 41 10.1016/j.procs.2025.05.156_bib5 Jianming (10.1016/j.procs.2025.05.156_bib1) 2023; 44 10.1016/j.procs.2025.05.156_bib4 Bingli (10.1016/j.procs.2025.05.156_bib7) 2023; 46 Yue (10.1016/j.procs.2025.05.156_bib8) 2023; 41 Bo (10.1016/j.procs.2025.05.156_bib9) 2023; 37 |
| References_xml | – volume: 44 start-page: 53 year: 2023 end-page: 61 ident: bib1 article-title: Automatic driving trajectory prediction based on reinforcement learning based on directed graph publication-title: Journal of Zhengzhou University: Engineering Edition – volume: 11 start-page: 1 year: 2023 end-page: 4 ident: bib2 article-title: Adaptive planning and Control Method of Autonomous Vehicle based on Reinforcement Learning publication-title: Volkswagen – volume: 37 start-page: 1 year: 2023 end-page: 10 ident: bib9 article-title: Research on autonomous driving motion planning based on reinforcement learning under uncertain environment publication-title: Journal of Chongqing University of Technology (Natural Science) – volume: 41 start-page: 172 year: 2024 end-page: 178 ident: bib10 article-title: End-to-end automatic driving based on LSTM deep reinforcement Learning publication-title: Computer Simulation – volume: 2 start-page: 75 year: 2023 end-page: 91 ident: bib11 article-title: Human-in-loop deep reinforcement Learning algorithm and its Application to intelligent Decision making in autonomous driving publication-title: Engineering (English) – reference: Shi Gaosong, Zhao Qinghai, Dong Xin, et al. Autopilot based on PPO algorithm man-machine interactive reinforcement learning method. Computer application research, 2024 (9):25-29. – volume: 46 start-page: 865 year: 2023 end-page: 872 ident: bib7 article-title: Horizontal control for trajectory tracking based on deep reinforcement learning publication-title: Journal of Hefei University of Technology: Natural Science Edition – volume: 41 start-page: 67 year: 2023 end-page: 75 ident: bib8 article-title: Following behavior modeling of autonomous vehicle based on deep reinforcement learning publication-title: Traffic Information and Safety – reference: Ning Qiang, Liu Yuansheng, Xie Longyang. Automatic vehicle control method based on SAC application. Computer engineering and application, 2023, 59 (8): 9. – volume: 37 start-page: 24 year: 2024 end-page: 26 ident: bib6 article-title: RMB based on reinforcement learning algorithm of automatic driving study publication-title: Industrial control computer – volume: 68 start-page: 8 year: 2024 end-page: 14 ident: bib3 article-title: Research on energy saving control strategy of urban rail trains based on Sarsa algorithm publication-title: Railway Standard Design – volume: 68 start-page: 8 issue: 8 year: 2024 ident: 10.1016/j.procs.2025.05.156_bib3 article-title: Research on energy saving control strategy of urban rail trains based on Sarsa algorithm publication-title: Railway Standard Design – volume: 46 start-page: 865 issue: 7 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib7 article-title: Horizontal control for trajectory tracking based on deep reinforcement learning publication-title: Journal of Hefei University of Technology: Natural Science Edition – volume: 44 start-page: 53 issue: 5 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib1 article-title: Automatic driving trajectory prediction based on reinforcement learning based on directed graph publication-title: Journal of Zhengzhou University: Engineering Edition – volume: 11 start-page: 1 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib2 article-title: Adaptive planning and Control Method of Autonomous Vehicle based on Reinforcement Learning publication-title: Volkswagen – volume: 37 start-page: 24 issue: 3 year: 2024 ident: 10.1016/j.procs.2025.05.156_bib6 article-title: RMB based on reinforcement learning algorithm of automatic driving study publication-title: Industrial control computer – volume: 41 start-page: 172 issue: 2 year: 2024 ident: 10.1016/j.procs.2025.05.156_bib10 article-title: End-to-end automatic driving based on LSTM deep reinforcement Learning publication-title: Computer Simulation – volume: 41 start-page: 67 issue: 2 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib8 article-title: Following behavior modeling of autonomous vehicle based on deep reinforcement learning publication-title: Traffic Information and Safety – ident: 10.1016/j.procs.2025.05.156_bib4 – volume: 37 start-page: 1 issue: 11 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib9 article-title: Research on autonomous driving motion planning based on reinforcement learning under uncertain environment publication-title: Journal of Chongqing University of Technology (Natural Science) – ident: 10.1016/j.procs.2025.05.156_bib5 – volume: 2 start-page: 75 year: 2023 ident: 10.1016/j.procs.2025.05.156_bib11 article-title: Human-in-loop deep reinforcement Learning algorithm and its Application to intelligent Decision making in autonomous driving publication-title: Engineering (English) |
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| SubjectTerms | Autonomous driving DWA algorithm Reinforcement learning algorithm Vehicle trajectory planning |
| Title | Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm |
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