Trajectory Autoencoder

Trajectory autoencoders are neural network models designed to learn compressed representations of trajectories, enabling efficient storage, generation, and manipulation of movement data across various domains. Current research emphasizes incorporating physics-based constraints into these models (e.g., using physics-informed loss functions) to ensure the generated trajectories are realistic and adhere to physical laws, as well as exploring multi-task learning and semi-supervised approaches to improve model performance and generalization. This work has significant implications for applications such as robotics, autonomous driving, and traffic flow analysis, offering improved data efficiency, enhanced simulation capabilities, and more explainable representations of complex movements.