用图神经网络让少量联网车协同控制，大幅抑制交通震荡波。
GNN-based Multi-Agent Control of Traffic Shockwaves in Sparse Vehicular Ad-hoc Networks
- 基于图神经网络的分布式多智能体强化学习框架
- 仅10%联网车时，震荡波传播减少80%
- 适合早期车联网部署，无需全局路况信息
交通震荡波是车辆流中向后传播的停走波，是现代交通系统拥堵、燃油效率低下和事故率上升的主要原因。尽管联网与自动驾驶车辆（CAVs）为缓解此类震荡波提供了可能，但多数现有控制策略依赖全局交通状态信息，难以在车联网（VANETs）早期部署阶段应用。本文提出一种基于图神经网络（GNN）的去中心化多智能体强化学习（MARL）框架，增强联网与自动驾驶车辆的控制架构。该方法使车辆仅通过局部信息及与邻近车辆交互即可学习协作控制策略。在真实高速公路交通条件下，通过可扩展仿真环境评估所提方案的有效性。仿真结果表明，在仅有10%车辆联网的情况下，该基于GNN的MARL框架可将交通震荡波传播减少高达80%。
原文摘要 · Abstract (English)
Traffic shockwaves are stop-and-go waves that propagate upstream through the streams of vehicles and are one of the major causes of traffic congestion, fuel inefficiency, and increased accident rates in modern transportation systems. Although Connected and Autonomous Vehicles (CAVs) offer a promising opportunity to mitigate such shockwaves, most existing control strategies rely on global traffic state information, making them impractical for early-stage deployment of Vehicular Ad-hoc Networks (VANETs). In this paper, we propose a decentralized Multi-Agent Reinforcement Learning (MARL) framework that integrates a Graph Neural Network (GNN) to enhance the control architecture of connected and autonomous vehicles. The proposed approach enables vehicles to learn cooperative control policies using locally available information and interaction with neighboring vehicles. The effectiveness of the proposed scheme is evaluated using a scalable simulation environment under realistic highway traffic conditions. Simulation results show that the proposed GNN-based MARL framework can reduce the propagation of traffic shockwaves by up to 80%, even when only 10% of the vehicles are connected.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。