Multi Vehicle

Multi-vehicle control focuses on developing algorithms enabling safe and efficient coordination of multiple autonomous vehicles in diverse environments, aiming to prevent collisions and optimize traffic flow. Current research heavily utilizes deep reinforcement learning, graphical neural networks, and game-theoretic approaches, often incorporating risk assessment and multi-modal predictions to handle the complexities of human-like driving behavior. These advancements are crucial for improving autonomous driving systems, particularly in unstructured environments and high-risk scenarios like dense traffic and unsignalized intersections, ultimately contributing to safer and more efficient transportation systems.