Hierarchical Pairwise and Group-Wise Interaction Modeling for Efficient Pedestrian Trajectory Prediction

Abstract

Accurate trajectory prediction in multi-agent environments is fundamental to understanding and forecasting collective motion in complex scenes. However, existing methods often struggle to capture the dynamic nature of interactions and higher-order group relationships. To address these challenges, we propose a novel framework that seamlessly integrates pairwise and group- wise interaction modeling. Our approach introduces a Step- wise Pairwise Interaction Modeling (SPIM) module to effectively capture dynamic interaction features over time, and a Hypergraph-based Global Interaction Modeling (HGIM) module to effectively model higher-order group relationships using adaptive hypergraph structures. Through the integration of these complementary modules within a unified multimodal decoder architecture, our method achieves competitive performance on the SDD and ETH-UCY datasets while significantly reducing model complexity. These results highlight the superior efficiency and effectiveness of our proposed approach.