🤖 AI Summary
This study addresses the challenges posed by traffic shockwaves, which exacerbate congestion, reduce fuel efficiency, and elevate accident risks. Existing control strategies often rely on global traffic information, limiting their applicability in sparse vehicular ad hoc networks (VANETs). To overcome this limitation, this work proposes a decentralized cooperative control framework that integrates graph neural networks (GNNs) with multi-agent reinforcement learning (MARL), enabling connected automated vehicles to effectively suppress shockwave propagation using only local observations and interactions with neighboring vehicles. Notably, this approach is the first to incorporate GNNs into MARL under sparse VANET conditions, substantially enhancing practicality for early-stage deployment. Simulation results on a highway scenario with only 10% market penetration demonstrate up to an 80% reduction in shockwave propagation, confirming the method’s efficacy and scalability.
📝 Abstract
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.