Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach

The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle’s driving behaviour will no longer be solely based on the driver’s limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.

• Record URL:
  https://doi.org/10.1109/TITS.2019.2942014
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • Zhou, Mofan
  • Yu, Yang
  • Qu, Xiaobo
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 433-443
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 21
  • Issue Number: 1
  • 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: Car following; Connected vehicles; Intelligent vehicles; Machine learning; Mathematical models; Mathematical oscillations; Neural networks; Signalized intersections; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01732471
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Feb 28 2020 5:10PM