Stability-based analysis of autonomous intersection management with pedestrians

With the development of vehicle-to-infrastructure and vehicle-to-vehicle technologies, vehicles will be able to communicate with the controller at the intersection. Autonomous driving technology enables vehicles to follow the instructions sent from the controller precisely. Autonomous intersection management (AIM) considers each vehicle as an agent and coordinates vehicle trajectories to resolve vehicle conflicts inside an intersection. This study proposes an autonomous intersection management algorithm called AIM-ped considering both vehicles and pedestrians which is able to produce the total optimal throughput when combined with max pressure control. This study analyzes the stability properties of the algorithm based on a simpler version of AIM-ped, which is a conflict region model of the autonomous intersection management. To implement the proposed algorithm in simulation, this study combines the max-pressure control with an existing trajectory optimization algorithm to calculate optimal vehicle trajectories. Simulations are conducted to test the effects of pedestrian demand on intersection efficiency. The simulation results show that delays of pedestrians and vehicles are negatively correlated and the proposed algorithm can adapt to the change in the pedestrian demand and activate vehicle movements with conflicting trajectories.

• Record URL:
  https://doi.org/10.1016/j.trc.2020.01.016
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X19309623
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2020 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Chen, Rongsheng
  • Hu, Jeffrey
  • Levin, Michael W
  • Rey, David
• Publication Date: 2020-5

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References;
• Pagination: pp 463-483
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 114
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Highway traffic control; Intelligent agents; Intelligent transportation systems; Optimization; Pedestrian flow; Pedestrian vehicle interface; Pedestrians; Signalized intersections; Traffic conflicts; Trajectory control; Vehicle to infrastructure communications; Vehicle to vehicle communications
• Subject Areas: Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01736391
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 20 2020 10:57AM