Agent Trajectory

Agent trajectory research focuses on predicting and understanding the movement patterns of individual and multiple agents, aiming to improve the accuracy and robustness of these predictions in complex, dynamic environments. Current research emphasizes developing advanced models, such as graph convolutional networks, diffusion models, and normalizing flows, often incorporating techniques like contrastive learning and variational inference to handle uncertainty and interactions between agents. This field is crucial for applications in autonomous driving, robotics, sports analytics, and security, enabling safer and more efficient systems by improving prediction accuracy and understanding agent behavior in various contexts.