Motion Latent Diffusion for Stochastic Trajectory Prediction
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)
摘要
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.
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关键词
pedestrian trajectory prediction,diffusion model,CVAE
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