Leveraging Maps of Spatial Motion Patterns to Enhance Long-Term Adaptive Trajectory Prediction with Diffusion Models

Pedestrian trajectory prediction is crucial for human-robot interaction applications. In this paper, we employ maps of spatial motion patterns combined with diffusion models to improve prediction accuracy. Our method projects both input and output trajectories into an embedding space, leveraging spatial motion patterns to bias anchors that represent the general motion flow within corresponding environments. These place-dependent spatial motion patterns are previously learned from prior environmental observations, enabling better adaptation to new surroundings. Diffusion models are then applied to generate multi-modal trajectory predictions based on the anchors. Experimental results demonstrate that our method outperforms existing models in domain adaptation and long-term trajectory prediction, showcasing the effectiveness of maps of spatial motion patterns in adapting to new environments and improving trajectory prediction performance.