Emergent Cooperative Driving Strategies for Stop-and-Go Wave Mitigation via Multi-Agent Reinforcement Learning

TL;DR

This paper presents a novel multi-agent reinforcement learning approach to develop cooperative driving strategies that mitigate stop-and-go traffic waves, improving flow stability and efficiency in simulated environments.

Contribution

It introduces a cooperative multi-agent DRL strategy for traffic wave mitigation and demonstrates its effectiveness in both simulation and classical car-following models.

Findings

Cooperative strategies reduce traffic wave propagation.

Buffer vehicles improve overall traffic stability.

Enhanced traffic throughput and stability in simulations.

Abstract

Stop-and-go waves in traffic flow pose a persistent challenge, compromising safety, efficiency, and environmental sustainability. This paper introduces a novel mitigation strategy discovered through training multi-agent deep reinforcement learning (DRL) agents in a simulated ring-road environment. The agents autonomously develop a cooperative driving policy, where most vehicles maintain minimal headways to maximize throughput, while a single "buffer" vehicle adopts a larger headway to absorb perturbations and prevent wave propagation. This strategy enhances stability without sacrificing overall flow. We further demonstrate that adapting this cooperative strategy to classical car-following models, such as the Intelligent Driver Model (IDM), yields improved stability and traffic efficiency. Furthermore, we show within a parametrised linear framework, that the cooperative strategy can optimise system performance under stability constraints. Our findings offer promising insights for future autonomous vehicle systems and highway management.