Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, the authors propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. The framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. The authors present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3096878
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Goulet, Nathan
  • Ayalew, Beshah
• Publication Date: 2022-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 10887-10901
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 8
  • 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: Connected vehicles; Energy consumption; Intelligent vehicles; Maneuvering; Predictive models; Traffic flow
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01868954
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 30 2022 9:16AM