A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways

TL;DR

This paper presents a multi-agent deep reinforcement learning framework to coordinate connected and automated vehicles at highway merge points, aiming to eliminate stop-and-go traffic and improve flow efficiency.

Contribution

It introduces a decentralized multi-agent deep reinforcement learning approach for CAV coordination at merging roads, demonstrating its effectiveness through simulations.

Findings

Achieved smooth traffic flow by eliminating stop-and-go driving.

Demonstrated the effectiveness of multi-agent deep RL in vehicle coordination.

Provided simulation results showing improved traffic efficiency.

Abstract

The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and automated vehicles (CAVs), have the potential to directly address these issues and improve transportation network efficiency and safety. In this paper, we consider a highway merging scenario and propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We use a decentralized form of the actor-critic approach to deep reinforcement learning$-$multi-agent deep deterministic policy gradient. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving. Videos and plots of the simulation results can be found at this supplemental $\href{https://sites.google.com/view/ud-ids-lab/MADRL}{\text{site}}$.