Control of Connected and Autonomous Vehicles for Congestion Reduction in Mixed Traffic: A Learning-Based Approach

Building on prior work on lane keeping and lane changing for connected and autonomous vehicles, this collaborative research project aims at taking a significant step forward to develop innovative learning-based, real-time control algorithms for connected and autonomous vehicles to reduce traffic congestion. This project aims at achieving four major objectives: (1) developing a traffic light prediction method by utilizing advanced deep learning techniques; (2) developing a trajectory optimization framework for a stream of vehicles to efficiently reduce the traffic congestion, attenuate the stop-and-go waves, and increase the throughput of the traffic; (3) integrating reinforcement learning techniques with (control) barrier functions to address the safety-oriented learning-based trajectory tracking control of autonomous vehicles; (4) validating the proposed congestion-reducing scheme with real-world vehicle trajectory data and SUMO testing under different environments in the presence of different vehicle mixes and driver uncertainties.

• Record URL:
  https://c2smarter.engineering.nyu.edu/control-of-connected-and-autonomous-vehicles-for-congestion-reduction-in-mixed-traffic-a-learningbased-approach

Language

• English

Project

• Status: Active
• Funding: $135050
• Contract Numbers:

  69A3551747124

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201
• Project Managers:

  Pohl, Lizzie

• Performing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201

  NYU

  NYC, NY  United States 
• Principal Investigators:

  Jiang, Zhong-Ping

  Ozbay, Kaan

• Start Date: 20231001
• Expected Completion Date: 20240930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Congestion management systems; Connected vehicles; Machine learning; Trajectory control; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01897925
• Record Type: Research project
• Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
• Contract Numbers: 69A3551747124
• Files: UTC, RIP
• Created Date: Oct 30 2023 10:33PM