Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey | |
Wu, Jingda4; Huang, Chao3; Huang, Hailong4; Lv, Chen2; Wang, Yuntong1; Wang, Fei-Yue1 | |
刊名 | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
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2024-07-01 | |
卷号 | 164页码:28 |
关键词 | Autonomous driving Reinforcement learning Behavior planning Decision Autonomous vehicle |
ISSN号 | 0968-090X |
DOI | 10.1016/j.trc.2024.104654 |
通讯作者 | Huang, Chao(hchao.huang@polyu.edu.hk) |
英文摘要 | Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) emerges as a pivotal tool in crafting these BP strategies. This paper offers a comprehensive review of RL-based BP strategies, spotlighting advancements from 2021 to 2023. We completely organize and distill the relevant literature, emphasizing paradigm shifts in RL-based BP. Introducing a novel categorization, we trace the trajectory of efforts aimed at surmounting practical challenges encountered by autonomous vehicles through innovative RL techniques. To guide readers, we furnish a quantitative analysis that maps the volume and diversity of recent RL configurations, elucidating prevailing trends. Additionally, we delve into the imminent challenges and potential directions for the future of RL-driven BP in AD. These directions encompass addressing safety vulnerabilities, fostering continual learning capabilities, enhancing data efficiency, championing collaborative vehicular cloud networks, integrating large language models, and enhancing ethical considerations. |
资助项目 | CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.[P0048792] |
WOS关键词 | DECISION-MAKING ; SAFE ; VEHICLES ; MODEL ; SCENARIOS ; POLICIES ; EFFICIENT ; BARRIER |
WOS研究方向 | Transportation |
语种 | 英语 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS记录号 | WOS:001244862600001 |
资助机构 | CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/58720] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
通讯作者 | Huang, Chao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Nanyang 639798, Singapore 3.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China 4.Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong, Peoples R China |
推荐引用方式 GB/T 7714 | Wu, Jingda,Huang, Chao,Huang, Hailong,et al. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2024,164:28. |
APA | Wu, Jingda,Huang, Chao,Huang, Hailong,Lv, Chen,Wang, Yuntong,&Wang, Fei-Yue.(2024).Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,164,28. |
MLA | Wu, Jingda,et al."Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 164(2024):28. |
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