DiffWT: Diffusion-Based Pedestrian Trajectory Prediction With Time-Frequency Wavelet Transform

Accurate pedestrian trajectory prediction is a crucial task for ensuring the safety of autonomous driving. However, most of the existing methods only model pedestrian trajectories in the spatial-temporal domain, which results in a lack of analysis of motion at different scales. In this work, we propose a framework based on wavelet transform and diffusion model, which is called DiffWT. Different from previous approaches, our method employs a discrete wavelet transform (DWT) to perform time-frequency analysis of trajectories. The high-frequency component of the DWT indicates local motion details of the trajectory, while the low-frequency one represents overall motion trends. Second, we propose a trajectory decoder comprising a conditional diffusion model and a cross-constrained bidirectional trajectory generator (C2Bid). The diffusion model generates the distribution of implicit pedestrian behaviors by taking the multiscale motion behaviors as conditions. Furthermore, the C2Bid module is designed as a cross-constrained bidirectional structure to decode behavioral distribution into multimodal trajectories. This trajectory decoder can generate precise distribution of trajectories and reduce accumulation of prediction errors. Extensive experimental results on the ETH/UCY and the Stanford drone datasets (SDDs) demonstrate that our method achieves better performance as well as higher efficiency compared to other state-of-the-art approaches.