Motion Prediction

Motion prediction aims to forecast the future movement of objects, primarily in autonomous driving and human-robot interaction contexts. Current research emphasizes improving prediction accuracy and robustness, particularly using transformer-based architectures, diffusion models, and Bayesian methods, often incorporating multimodal data (e.g., images, LiDAR, text) to enhance contextual understanding and address challenges like occlusion and uncertainty quantification. These advancements are crucial for enhancing the safety and efficiency of autonomous systems and enabling more natural and safe human-robot collaboration.