Driving Tasks Transfer Using Deep Reinforcement Learning for Decision-Making of Autonomous Vehicles in Unsignalized Intersection

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred through three transfer rules in another driving mission and evaluated. Simulation results reveal that the decision-making strategies related to similar tasks are transferable and have a high success rate. It indicates that the presented control framework could reduce time consumption and realize online implementation. Therefore, the transfer RL concept is helpful for establishing the real-time decision-making policy for autonomous vehicles.

• Record URL:
  http://dx.doi.org/10.1109/TVT.2021.3121985
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Shu, Hong
  • Liu, Teng
  • Mu, Xingyu
  • Cao, Dongpu
• Publication Date: 2022-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 41-52
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 71
  • Issue Number: 1
  • 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: Autonomous vehicles; Decision making; Driving; Machine learning; Task analysis; Unsignalized intersections
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01837154
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 25 2022 8:58AM