Vehicle Motion Prediction

Vehicle motion prediction aims to accurately forecast the future trajectories of vehicles, a crucial task for autonomous driving and traffic management. Current research emphasizes improving prediction accuracy and reliability, particularly in handling complex interactions between vehicles and incorporating map information to avoid unrealistic "off-road" predictions. This involves exploring various model architectures, including transformers, attention mechanisms, and Gaussian process-based methods, often combined with occupancy grid maps and Frenet coordinate systems for improved representation and efficiency. Advances in this field directly impact the safety and efficiency of autonomous vehicles and contribute to a deeper understanding of multi-agent dynamics in complex environments.