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. Secondly, we propose a trajectory decoder comprising a conditional diffusion model and a cross-constrained bi-directional trajectory generator (C2Bid). The diffusion model generates the distribution of implicit pedestrian behaviors by taking the multi-scale motion behaviors as conditions. Furthermore, the C2Bid module is designed as a cross-constrained bi-directional 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 (SDD) demonstrate that our method achieves better performance as well as higher efficiency compared to other state-of-the-art approaches.