Emergent Cooperative Driving Strategies for Stop-and-Go Wave Mitigation via Multi-Agent Reinforcement Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908662282321920 |
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| author | Korbmacher, Raphael Straub, Daniel Tordeux, Antoine Totzeck, Claudia |
| author_facet | Korbmacher, Raphael Straub, Daniel Tordeux, Antoine Totzeck, Claudia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emergent Cooperative Driving Strategies for Stop-and-Go Wave Mitigation via Multi-Agent Reinforcement Learning Korbmacher, Raphael Straub, Daniel Tordeux, Antoine Totzeck, Claudia Physics and Society 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. |
| title | Emergent Cooperative Driving Strategies for Stop-and-Go Wave Mitigation via Multi-Agent Reinforcement Learning |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2511.14378 |