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Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey

文献类型：期刊论文

作者Wu, Jingda1; Huang, Chao2; Huang, Hailong1; Lv, Chen3; Wang, Yuntong4; Wang, Fei-Yue4
刊名TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
出版日期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
DOI10.1016/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.
WOS关键词DECISION-MAKING ; SAFE ; VEHICLES ; MODEL ; SCENARIOS ; POLICIES ; EFFICIENT ; BARRIER
资助项目CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.[P0048792]
WOS研究方向Transportation
语种英语
WOS记录号WOS:001244862600001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构CATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.
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.Hong Kong Polytech Univ, Dept Aeronaut & Aviat Engn, Hong Kong, Peoples R China
2.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
3.Nanyang Technol Univ, Sch Mech & Aerosp Engn, Nanyang 639798, Singapore
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, 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.

入库方式： OAI收割

来源：自动化研究所

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