Cooperative Trajectory Planning

Cooperative trajectory planning focuses on developing algorithms that enable multiple agents, such as autonomous vehicles or robots, to safely and efficiently navigate shared environments by coordinating their movements. Current research emphasizes robust methods for handling uncertainties, including human behavior and sensor limitations, often employing techniques like Monte Carlo Tree Search, deep reinforcement learning (e.g., TD3), and advanced optimization algorithms (e.g., iLQR, ADMM) to achieve real-time performance and scalability. This field is crucial for advancing autonomous systems in various domains, from automated driving and robotics to air and sea traffic management, by improving safety, efficiency, and overall system performance.