A Decision-Making Strategy for Vehicle Autonomous Braking in Emergency via Deep Reinforcement Learning

Autonomous braking through vehicle precise decision-making and control to reduce accidents is a key issue, especially in the early diffusion phase of autonomous vehicle development. This paper proposes a deep reinforcement learning (DRL)-based autonomous braking decision-making strategy in an emergency situation. Three key influencing factors, including efficiency, accuracy and passengers' comfort, are fully considered and satisfied by the proposed strategy. First, the vehicle lane-changing process and the braking process are analyzed in detail, which include the critical factors in the design of the autonomous braking strategy. Second, the authors propose a DRL process that determines the optimal strategy for autonomous braking. Particularly, a multi-objective reward function is designed, which can compromise the rewards achieved of different brake moments, the degree of the accident, and the comfort of the passenger. Third, a typical actor-critic (AC) algorithm named deep deterministic policy gradient (DDPG) is adopted for solving the autonomous braking problem, which can improve the efficiency of the optimal strategy and be stable in continuous control tasks. Once the strategy is well trained, the vehicle can automatically take optimal braking behavior in an emergency to improve driving safety. Extensive simulations validate the effectiveness and efficiency of the authors' proposal in terms of learning effectiveness, decision-making accuracy and driving safety.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2020.2986005
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Fu, Yuchuan
  • Li, Changle
  • Yu, Fei Richard
  • Luan, Tom H
  • Zhang, Yao
• Publication Date: 2020-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5876-5888
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 69
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Brakes; Braking; Decision making; Highway safety; Machine learning; Neural networks; Vehicle safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01748451
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 24 2020 9:16AM