Motion Latent Diffusion for Stochastic Trajectory Prediction

Abstract

The indeterminacy of human motion poses challenges for pedestrian trajectory prediction. Consequently, existing methods adopt multimodal strategy to model pedestrians future trajectories. A significant advancement in this regard is the growing prominence of the diffusion model. However, the two-dimensional inputs for trajectory prediction not provide sufficient contextual information for the diffusion model. Furthermore, the diffusion model suffers from substantial inference time. To address these conundrums, we propose a trajectory prediction method based on the diffusion model, named as Motion Latent Diffusion (MLD). The core of MLD is the Conditional Variational Autoencoder (CVAE) to transform the original low-dimensional inputs into a higher-dimensional latent space, expanding the receptive field to yield more comprehensive and intricate representations. Simultaneously, during the inferential stage of the diffusion model, we adopt a leapfrogging inference strategy, which facilitates a faster sampling process. Experiments conducted on the ETH/UCY and Stanford Drone datasets (SDD) corroborate the superiority of our method.