#Trajectory Prediction

This compilation gathers advanced research materials on interaction-aware trajectory prediction, featuring datasets, academic papers, and public code useful across academia and industry. Covering insights into vehicle, pedestrian, and sports player scenarios, it is regularly updated by experts from Stanford University and UC Berkeley, facilitating collaboration. It also includes surveys on cutting-edge deep learning, neural networks, and autonomous driving technologies, with additional resources in reinforcement learning and decision-making, driving innovation forward.