Connected Autonomous Vehicle

Connected autonomous vehicles (CAVs) aim to improve traffic efficiency and safety through cooperative driving, leveraging vehicle-to-everything (V2X) communication and advanced perception. Current research heavily focuses on developing robust and efficient algorithms for cooperative decision-making, trajectory prediction, and collision avoidance, often employing deep reinforcement learning, transformer networks, and model predictive control. These advancements are crucial for addressing challenges like communication limitations, handling uncertainty in mixed-traffic environments, and ensuring safety guarantees, ultimately impacting the development of safer and more efficient transportation systems.