Intersection Self-Organization Control for Connected Autonomous Vehicles based on Traffic Strategy Learning Algorithm

With the rapid advancement of intelligent vehicles and vehicular communication systems, connected autonomous vehicles (CAVs) will run on the road in the foreseeable future. To increase the traffic efficiency of CAVs at intersections, it is necessary to apply a new method to replace the traditional signal time assignment. This paper proposes a general solution for CAVs passing through non-signalized intersections effectively. A novel idea is developed to use a traffic strategy learning algorithm for real-time decision-making. Through an image representation method based on lanes reordering for intersection state description, the convolutional neural network model is adopted. The proposed methods can take full advantage of spatiotemporal resources at the intersection and ensure the rapidity and efficiency for practical applications. Several numerical experiments in different traffic situations are designed to demonstrate the validity of the proposed method.

• Record URL:
  • https://doi.org/10.1061/9780784482292.478
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784482292
• Supplemental Notes:
  • © 2019 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Cai, Pinlong
  • Wang, Yunpeng
  • Lu, Guangquan
• Conference:
  • 19th COTA International Conference of Transportation Professionals
  • Location: Nanjing , China
  • Date: 2019-7-6 to 2019-7-8
• Publication Date: 2019-7

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 5551-5562
• Monograph Title: CICTP 2019: Transportation in China—Connecting the World

Subject/Index Terms

• TRT Terms: Algorithms; Highway traffic control; Intelligent transportation systems; Intelligent vehicles; Machine learning; Unsignalized intersections
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01711821
• Record Type: Publication
• ISBN: 9780784482292
• Files: TRIS, ASCE
• Created Date: Jul 22 2019 10:32AM