Game Theoretic Trajectory

Game-theoretic trajectory planning focuses on designing robot and autonomous vehicle paths that account for the interactive behaviors of other agents, aiming for safe and socially acceptable navigation in shared environments. Current research emphasizes developing efficient algorithms, such as those based on neural networks, particle swarm optimization, and Monte Carlo Tree Search, to solve the complex optimization problems inherent in multi-agent interactions, often within frameworks of dynamic games and potential games. These advancements are crucial for enabling safe and efficient autonomous systems in diverse applications, from autonomous driving to multi-robot coordination, by improving both the safety and social acceptability of robotic navigation.