Spatio-Temporal Graph-Based Pedestrian Trajectory Prediction with Environmental Context Integration and Constraint Learning *
Accurate pedestrian trajectory prediction is fundamental for safe navigation of autonomous robots and vehicles in crowded environments. Although graph-based methods such as Social-STGCNN efficiently model social interactions among pedestrians, they largely ignore static environmental constraints such as walls and obstacles, which can lead to physically infeasible predictions. In this paper, we propose an extended trajectory prediction method that integrates local and global environmental information into Social-STGCNN via Multi-Head Cross-Attention, and incorporates environmental constraint learning through contrastive MapNCE and collision avoidance EnvCol losses. To generate diverse prediction while maintaining scene consistency, we further introduce a low-dimensional trajectory representation based on Singular Value Decomposition and Adaptive Anchors derived from K-means clustering. We evaluate the proposed method on the ETH/UCY benchmark across five scenes using Average Displacement Error (ADE), Final Displacement Error (FDE), obstacle collision rate, and inference time. The results show that the proposed method consistently improves the FDE over Social-STGCNN, while ADE increases. The obstacle collision rate also increases, revealing a trade-off between endpoint accuracy and full-trajectory environmental compliance.