2025
PhysGCN-DL: Physics-Informed Graph Convolutional Networks with Diversity-Aware Loss Optimization for Multimodal Pedestrian Trajectory Prediction
IROS 2025
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-D