Trajectory Embeddings

Trajectory embeddings represent sequences of locations and timestamps as vectors, facilitating analysis and prediction of movement patterns in various applications. Current research focuses on developing effective pre-training methods to learn generalizable embeddings from unlabeled data, often employing contrastive learning or attention-based architectures to capture spatio-temporal correlations and behavioral information. These advancements improve the performance of downstream tasks like trajectory prediction, particularly in challenging scenarios such as autonomous driving and human-robot interaction, by leveraging richer representations of movement.