MATRICS: A Multi-Agent Deep Reinforcement Learning-Based Traffic-Aware Intelligent Lane-Change System

Lokesh Das, Myounggyu Won

Abstract: We present MATRICS, a traffic-aware multi-agent reinforcement learning (MARL)-based intelligent lane-change system designed for autonomous vehicles (AVs). While existing research primarily focuses on enhancing the local impact of the ego vehicle’s lane-change decisions, MATRICS stands out by optimizing both local and global performance, i.e., aiming not only to improve the traffic efficiency, driving safety, and driver comfort of the ego vehicle, but also to enhance overall traffic flow within a designated road segment. Through an extensive review of the transportation literature, we construct a novel state space integrating local traffic information collected from surrounding vehicles and global traffic data obtained from roadside units (RSUs). We develop a reward function to guide judicious lane-change decisions, considering both ego vehicle performance and traffic flow enhancement. Our local density-aware multi-agent double deep Q-network (DDQN) algorithm facilitates effective cooperation among agents in executing lane-change maneuvers. Simulation results demonstrate MATRICS’ superior performance across metrics of traffic efficiency, driving safety, and driver comfort in comparison with a state-of-the-art MARL model.