Enhancing Universal Mixed-Autonomy Channel Modeling with Explainable Artificial Intelligence

In the forthcoming transportation landscape, the integration of Connected and Automated Vehicles (CAVs) with traditional human-driven traffic environments presents a multifaceted challenge. In this mixed-autonomy scenario, the coexistence of CAVs and Human-Driven Vehicles (HDVs) necessitates the sharing of road space and resources, all while striving to ensure safety and transportation efficiency. Within this dynamic context, the rapid and accurate prediction of channel quality becomes paramount for ensuring system stability and reliability. However, the inherent complexity and variability of such traffic environments introduce a multitude of interfering factors that conventional channel models struggle to address effectively. Thus, the development of a universal mixed-autonomy channel model that can adapt to diverse conditions and enhance communication quality is of paramount importance. Our proposed channel model, enhanced by Explainable Artificial Intelligence (XAI), encompasses system design of comprehensive mixed-autonomy environments, data collection, Machine Learning (ML) training, feature analysis using SHapley Additive exPlanation (SHAP), and performance validation. This model, underpinned by rigorous data-driven analysis, enables precise and efficient predictions of channel characteristics, offering a flexible and impactful solution that advances the intelligence of mixed-autonom systems and enhances communication reliability.