Physics-Informed Learning and Control of Connected and Autonomous Vehicles for Congestion Reduction

Building upon previous work in lane changing using physics-informed machine learning for autonomous vehicles, the goal of this project is to develop physics-informed machine learning and data-driven control-based tools for the combined longitudinal and lateral planning and control of connected and autonomous vehicles (CAVs). This research initiative holds the promise to have the following advantages: 1) Utilizing physics-informed machine learning as a tool can significantly enhance computational efficiency, which is beneficial for real-time control in complex scenarios; 2) Combining neural networks with physical models can greatly reduce over-reliance on data; 3) During the training phase of neural networks, any differentiable objective function and various constraints can be considered, allowing it to solve constrained multi-objective model predictive control problems without affecting computational speed. In addition, this project will design a lane-change decision-making module based on deep reinforcement learning and validate the congestion-reducing scheme using NGSIM data and SUMO simulations.

• Record URL:
  https://c2smarter.engineering.nyu.edu/physicsinformed-learning-and-control-of-connected-and-autonomous-vehicles-for-congestion-reduction/

Language

• English

Project

• Status: Active
• Funding: $147285
• 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

  Chase, Holly

• 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

  New York University Tandon School of Engineering

  6 Metrotech Center
  Brooklyn, NY  United States  11201
• Principal Investigators:

  Ozbay, Kaan

  Jiang, Zhong-Ping

• Start Date: 20240901
• Expected Completion Date: 20051130
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Congestion management systems; Connected vehicles; Machine learning; Neural networks; Traffic simulation
• Identifier Terms: SUMO (Traffic simulation model)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01937758
• 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: Nov 21 2024 5:29PM