CAVSim: A Microscopic Traffic Simulator for Evaluation of Connected and Automated Vehicles

Connected and automated vehicles (CAVs) are expected to play a vital role in the emerging intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs, and there is an urgent need for a generic and unified traffic simulator to simulate and evaluate these methods. However, traditional traffic simulators have two critical deficiencies for CAV simulation needs: 1) the planning and dynamical modeling of vehicles in traditional simulators are based on a feedback mode, which is incompatible with the feed-forward decision and planning that CAVs commonly adopt; 2) the traditional simulators cannot provide typical traffic scenarios and corresponding standardized algorithms for multi-CAV cooperative driving. In this paper, the authors introduce CAVSim, a novel microscopic traffic simulator for CAVs, to address these deficiencies. CAVSim is developed modularly according to the emerging technology of the CAV environment, emphasizes feed-forward decision and planning for CAVs, and highlights the cooperative decision and planning components in the CAV environment. CAVSim incorporates rich and typical traffic scenarios and provides standardized cooperative driving algorithms and comparable performance metrics for multi-CAV cooperative driving. With CAVSim, researchers can conveniently deploy decision, planning, and control methods for CAVs at different levels, evaluate their performance, compare them with the standardized algorithms incorporated in CAVSim, and even further explore their impact on traffic flow. As a unified platform for CAVs, CAVSim can facilitate the studies on CAVs and promote the advancement of methods and techniques for CAVs.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2023.3273565
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Zhang, Jiawei
  • Chang, Cheng
  • He, Zimin
  • Zhong, Wenqin
  • Yao, Danya
  • Li, Shen
  • Li, Li
• Publication Date: 2023-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 10038-10054
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 9
  • 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; Connected vehicles; Microscopic traffic flow; Planning; Simulation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01897821
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 30 2023 8:55AM