Applications and Challenges of Reinforcement Learning in Autonomous Driving Technology

被引:0
|
作者
He Y. [1 ,2 ]
Lin H. [3 ]
Liu Y. [3 ]
Yang L. [2 ]
Qu X. [3 ]
机构
[1] School of Civil Engineering and Transportation, South China University of Technology, Guangzhou
[2] School of Information Engineering, Chang’an University, Xi’an
[3] School of Vehicle and Mobility, Tsinghua University, Beijing
来源
关键词
artificial intelligence; autonomous driving; reinforcement learning;
D O I
10.11908/j.issn.0253-374x.23397
中图分类号
学科分类号
摘要
This paper provides a comprehensive overview and summary of the application of reinforcement learning in the field of autonomous driving. First,an introduction to the principles and development of reinforcement learning is presented. Following that,the autonomous driving technology system and the fundamentals required for the application of reinforcement learning in this field are systematically introduced. Subsequently, application cases of reinforcement learning in autonomous driving are described according to different directions of use. Finally,the current challenges of applying reinforcement learning in the field of autonomous driving are deeply analyzed,and several prospects are proposed. © 2024 Science Press. All rights reserved.
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页码:520 / 531
页数:11
相关论文
共 83 条
  • [71] YAN X,, Et al., Corner case generation and analysis for safety assessment of autonomous vehicles[J], Transportation Research Record, 2675, 11, (2021)
  • [72] CHEN B, Et al., Adversarial evaluation of autonomous vehicles in lane-change scenarios [J], IEEE Transactions on Intelligent Transportation Systems, 23, 8, (2021)
  • [73] FENG S,, YAN X,, SUN H,, Et al., Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment [J], Nature Communications, 12, 1, (2021)
  • [74] CHEN Zeyu, FANG Zhiyuan, YANG Ruixin, Et al., Energy management strategy for hybrid electric vehicle based on the deep reinforcement learning method[J], Transactions of China Electrotechnical Society, 37, 23, (2022)
  • [75] Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle[J], Energy, 197, (2020)
  • [76] XIONG R, YU Q., Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle[J], Applied Energy, 211, (2018)
  • [77] Guodong YIN, Tong ZHU, REN Zuping, Et al., Intelligent control system framework for multi-agent based electric vehicle chassises[J], China Mechanical Engineering, 29, 15, (2018)
  • [78] JIANG Hong, WANG Pengcheng, LI Zhongxing, Research on air suspension vehicle height intelligent control system based on agent theory [J], Journal of Chongqing University of Technology( Natural Science), 33, 4, (2019)
  • [79] KALABIC U., Dynamics-enabled safe deep reinforcement learning:Case study on active suspension control, 2019 IEEE Conference on Control Technology and Applications(CCTA), pp. 585-591, (2019)
  • [80] A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge[J], Computer-Aided Civil and Infrastructure Engineering, 38, 8, (2023)