PhysGCN-DL: Physics-Informed Graph Convolutional Networks with Diversity-Aware Loss Optimization for Multimodal Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction ensures safe navigation in autonomous driving and intelligent robots. Existing methods have shown promising results but still face challenges in handling dynamic environments, social interactions, and high-dimensional data. In this paper, we propose a novel PhysGCN-DL within the itransformer framework to address these challenges. Our model incorporates physically-inspired dynamic interaction modeling by representing physical interactions between pedestrians as edge weights in graph convolution. This approach captures the heterogeneity of pedestrian movement and improves the interpretability of social interactions. Moreover, we design a novel loss function to jointly enhance prediction diversity and accuracy, thereby improving the model’s robustness across both dense and sparse scenarios. Empirical evaluations confirm that our approach outperforms existing methods in generating accurate and diverse pedestrian trajectories.