发表机构
Microsoft; GITAM University; National Institute of Technology, Rourkela（微软公司; 吉塔姆大学; 鲁尔克拉国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究稀疏车载自组织网络中交通激波问题，提出集成图神经网络的去中心化多智能体强化学习框架，车辆利用本地信息和邻车交互学习协同控制策略，能有效减少交通激波传播，即使低比例车辆联网时也能减少80%。
AI 中文摘要
交通激波是在车流中向上游传播的走走停停的波，是现代交通系统中交通拥堵、燃油效率低下和事故率增加的主要原因之一。虽然联网自动驾驶车辆（CAV）为减轻此类激波提供了契机，但大多数现有控制策略依赖全局交通状态信息，不适用于车载自组织网络（VANET）的早期部署。本文提出了一种去中心化的多智能体强化学习（MARL）框架，集成图神经网络（GNN）来增强联网自动驾驶车辆的控制架构。该方法使车辆能利用本地可用信息及与相邻车辆的交互学习协同控制策略。在现实高速公路交通条件下，使用可扩展模拟环境评估了该方案的有效性。模拟结果表明，即使只有10%的车辆联网，基于GNN的MARL框架也能将交通激波的传播减少高达80%。
英文摘要
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.
