Trajectory Augmentation

Trajectory augmentation focuses on enhancing datasets of movement paths (trajectories) to improve the performance of machine learning models, particularly in areas like reinforcement learning and imitation learning. Current research emphasizes generating synthetic trajectories that are both realistic and beneficial for training, employing techniques like diffusion models and geometric transformations while carefully considering constraints such as safety or reward maximization. This approach addresses data scarcity and distributional shift issues, leading to more robust and effective models for applications ranging from autonomous driving to mobility analysis.