HSIGCN: Hierarchical Spatial Interaction Graph Convolutional Network Considering Group Behavior for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is crucial in various fields, but remains challenging due to complex spatial interactions. While existing pedestrian trajectory prediction methods show promise, they often fail to capture these dynamics effectively. To address this limitation, a hierarchical spatial interaction graph convolutional network (HSIGCN) is proposed to handle both group interactions and spatial interactions. Although previous methods have attempted to model group behaviors, they lack a comprehensive consideration of group interactions and often oversimplify the complex social dynamics in groups. HSIGCN introduces a novel group interaction mechanism that encompasses four types of interactions: all-pedestrian, intragroup, out-group, and intergroup interactions, enhancing the expressiveness in group behavior prediction. Furthermore, current approaches to spatial interaction sparsification either rely solely on prior-based or on learning-based methods. HSIGCN innovatively combines both approaches to form a mixed sparsification mechanism, effectively filtering all-pedestrian and out-group interactions. Additionally, existing prior-based methods fail to consider social factors comprehensively. HSIGCN takes into account the field of view (FOV), collision awareness, and distance factors to establish a more robust prior-based sparse function. Experimental results on ETH and UCY datasets demonstrate that the proposed method significantly outperforms baseline models, showcasing its potential to accurately predict pedestrian trajectories by effectively handling complex spatial interactions.