xTL: Reducing Communication Overhead with XAI-Guided, Semantic-Aware DRL for Urban Traffic Light Control

Deep Reinforcement Learning (DRL) for traffic light control adaptively adjusts signals based on real-time traffic conditions to alleviate urban congestion. However, transmitting image data from intersection cameras results in high communication overhead and latency in practical deployments. Traditional DRL methods lack interpretability when selecting some common traffic features (e.g., traffic densities, speed) as state input. Inspired by 6G semantic communication that transmits semantic information rather than raw image data, this paper proposes xTL, an eXplainable AI (XAI)-guided DRL development approach to achieve semantic state design and address communication overhead. Unlike traditional DRL that heavily depends on empirical tuning, xTL allows human experts to efficiently guide and interpret model design by using XAI-generated explanations to distill lightweight semantic traffic features from image data. Utilizing SHapley Additive exPlanations (SHAP)-generated saliency maps, we identify a new critical feature: the location of the last vehicle in the first platoon on each incoming road, which can interpret intersection traffic dynamics and effectively improve traffic. Experiments on two urban intersections in SUMO demonstrate that xTL slashes communication costs by over 90% and shortens DRL training duration by about 21%, without compromising traffic control effectiveness.