Vehicle Coordination

Vehicle coordination research focuses on developing algorithms and control strategies for multiple vehicles to operate safely and efficiently, often in complex and dynamic environments. Current efforts explore decentralized and centralized approaches, employing techniques like game theory, deep reinforcement learning (e.g., TD3), and large language models for decision-making, alongside optimization methods such as Harris Hawks Optimization and mixed-integer programming. These advancements aim to improve traffic flow, grid stability (in the context of electric vehicle charging), and the safety and reliability of autonomous driving systems, impacting both transportation infrastructure and energy management.