Multi-task safe reinforcement learning for navigating intersections in dense traffic
文献类型：期刊论文
| 作者 | Liu, Yuqi1,2; Gao, Yinfeng3; Zhang, Qichao1,2 ; Ding, Dawei3; Zhao, Dongbin1,2
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| 刊名 | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 360期号:17页码:13737-13760 |
| ISSN号 | 0016-0032 |
| DOI | 10.1016/j.jfranklin.2022.06.052 |
| 通讯作者 | Zhang, Qichao(zhangqichao2014@ia.ac.cn) |
| 英文摘要 | Multi-task intersection navigation, which includes unprotected turning left, turning right, and going straight in heavy traffic, remains a difficult task for autonomous vehicles. For the human driver, negotiation skills with other interactive vehicles are the key to guaranteeing safety and efficiency. However, it is hard to balance the safety and efficiency of the autonomous vehicle for multi-task intersection navigation. In this paper, we formulate a multi-task safe reinforcement learning framework with social attention to improve the safety and efficiency when interacting with other traffic participants. Specifically, the social attention module is used to focus on the states of negotiation vehicles. In addition, a safety layer is added to the multi-task reinforcement learning framework to guarantee safe negotiation. We deploy experiments in the simulators SUMO, which has abundant traffic flows, and CARLA, which has high-fidelity vehicle models. Both show that the proposed algorithm improves safety while maintaining stable traffic efficiency for the multi-task intersection navigation problem. More details and demonstrations are available at https:// github.com/ liuyuqi123/ SAT.(c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved. |
| WOS关键词 | MODEL |
| 资助项目 | National Natural Science Foundation of China (NSFC)[62136008] ; National Natural Science Foundation of China (NSFC)[62173325] ; Beijing Science and Technology Plan[Z191100007419002] |
| WOS研究方向 | Automation & Control Systems ; Engineering ; Mathematics |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:001111154800001 |
| 资助机构 | National Natural Science Foundation of China (NSFC) ; Beijing Science and Technology Plan |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55110]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Qichao |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Coll Artificial Intelligence, Beijing 100049, Peoples R China 3.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 1000839, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yuqi,Gao, Yinfeng,Zhang, Qichao,et al. Multi-task safe reinforcement learning for navigating intersections in dense traffic[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,2023,360(17):13737-13760. |
| APA | Liu, Yuqi,Gao, Yinfeng,Zhang, Qichao,Ding, Dawei,&Zhao, Dongbin.(2023).Multi-task safe reinforcement learning for navigating intersections in dense traffic.JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,360(17),13737-13760. |
| MLA | Liu, Yuqi,et al."Multi-task safe reinforcement learning for navigating intersections in dense traffic".JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 360.17(2023):13737-13760. |
入库方式： OAI收割
来源：自动化研究所
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