Connected and Automated Vehicle

Connected and automated vehicles (CAVs) aim to improve traffic efficiency and safety through vehicle-to-everything (V2X) communication and autonomous driving capabilities. Current research heavily focuses on developing robust decision-making algorithms, often employing multi-agent reinforcement learning (MARL) with attention mechanisms and intent sharing, to address challenges like intersection management and car-following in mixed human-CAV traffic. These advancements are crucial for mitigating traffic oscillations, optimizing throughput at intersections, and enhancing safety features such as pedestrian collision warning systems, ultimately impacting transportation systems and public safety.