Confidence-Aware Reinforcement Learning for Self-Driving Cars

Reinforcement learning (RL) can be used to design smart driving policies in complex situations where traditional methods cannot. However, they are frequently black-box in nature, and the resulting policy may perform poorly, including in scenarios where few training cases are available. In this paper, the authors propose a method to use RL under two conditions: (i) RL works together with a baseline rule-based driving policy; and (ii) the RL intervenes only when the rule-based method seems to have difficulty handling and when the confidence of the RL policy is high. Their motivation is to use a not-well trained RL policy to reliably improve AV performance. The confidence of the policy is evaluated by Lindeberg-Levy Theorem using the recorded data distribution in the training process. The overall framework is named “confidence-aware reinforcement learning” (CARL). The condition to switch between the RL policy and the baseline policy is analyzed and presented. Driving in a two-lane roundabout scenario is used as the application case study. Simulation results show the proposed method outperforms the pure RL policy and the baseline rule-based policy.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3069497
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Cao, Zhong
  • Xu, Shaobing
  • Peng, Huei
  • Yang, Diange
  • Zidek, Robert
• Publication Date: 2022-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 7419-7430
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Planning; Traffic engineering; Trajectory; Trajectory control
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01860134
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2022 2:27PM