A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways
This addresses traffic efficiency and safety problems for highway systems, but it is incremental as it applies existing methods to a specific scenario.
The paper tackles traffic congestion and safety issues at highway merging points by proposing a multi-agent deep reinforcement learning framework to coordinate connected and automated vehicles, achieving smooth traffic flow by eliminating stop-and-go driving.
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 $\href{https://sites.google.com/view/ud-ids-lab/MADRL}{\text{site}}$.