Abstract: Current pedestrian trajectory prediction faces two major challenges:1)the difficulty in modeling the interrelationships among multiple pedestrians and the impact of complex environmental states;2)the increased model scale,which hinders its effectiveness in resource-constrained scenarios such as autonomous vehicles.To address these challenges more effectively,this study proposes a Multi-stage Pedestrian Trajectory Prediction framework,abbreviated as MSPP-Net.The framework comprises three components:a student module,a teacher module,and a social interaction module.Firstly,the student module constructs a prediction model based on wavelet transforms,decomposing pedestrian trajectories into high-frequency and low-frequency features to accurately extract motion details and global trends.Simultaneously,the teacher model is trained on multimodal trajectory data,including trajectories,poses,text,and the student model enhances its prediction performance by learning from the teacher model through knowledge distillation.Secondly,a social interaction module based on dynamic differential equations is developed to capture the dynamic characteristics of pedestrian movements,further improving the rationality of predictions,thus forming the final MSPP-Net prediction model.Finally,extensive experiments on the ETH/UCY and SDD datasets demonstrate that MSPP-Net achieves improvements of 12.50%/2.63% and 19.30%/10.34% in ADE and FDE metrics,respectively,outperforming mainstream methods,while reducing the parameter count by 64.47% compared to the teacher model.

Key words: Pedestrian trajectory prediction, Knowledge distillation, Wavelet transform, Dynamic differential equations, Transformer