Assessing Benefits of Mixed Traffic Platooning with Multi-Agent Reinforcement Learning and Cooperative Adaptive Cruise Control with Unconnected Vehicles

This project assesses the performance benefits of implementing multi-agent reinforcement learning (MARL)-based cooperative platooning and cooperative adaptive cruise control with unconnected vehicles (CACCu) in mixed traffic environments, comparing scenarios with and without these technologies under various connected automated vehicle (CAV) market penetrations. The main goal is to investigate when a policy to deploy these advanced technologies makes sense. Cooperative platooning in mixed traffic, where CAVs must interact safely and efficiently with human-driven vehicles, remains a key barrier to realizing the full mobility, safety, and energy benefits of connected automation, a challenge amplified by uncertainty in human driving behavior. When a CAV’s immediate preceding vehicle is not connected, it may benefit from a lane change to follow a connected vehicle and form cooperative adaptive cruise control; the team’s MARL approach, built on a CNN QMIX architecture supporting centralized training with decentralized execution, learns coordination policies that adapt to surrounding vehicles rather than relying on fixed rules.

• Record URL:
  • https://smartercenter.org/about-2/projects/assessing-benefits-of-mixed-traffic-platooning-with-multi-agent-reinforcement-learning-and-cooperative-adaptive-cruise-control-with-unconnected-vehicles/

Language

• English

Project

• Status: Active
• Funding: $200,000.00
• Contract Numbers:

  69A3552348303

• 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:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Performing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Principal Investigators:

  Park, Brian

• Start Date: 20260801
• Expected Completion Date: 20280301
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Machine learning; Multi-agent systems; Traffic platooning; Traffic simulation; Vehicle mix
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01997626
• Record Type: Research project
• Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
• Contract Numbers: 69A3552348303
• Files: UTC, RIP
• Created Date: Jul 30 2026 4:05PM