Group commonality graph: Multimodal pedestrian trajectory prediction via deep group features

Pedestrian trajectory prediction is a challenging task in domains such as autonomous driving and robot motion planning. Existing methods often focus on aggregating nearby individuals into a single group, while neglecting individual differences and the risks of unreliable interactions. Therefore we propose a novel framework termed group commonality graph, which comprises a group feature capture network and a spatial-temporal graph sparse connected network. The previous network can group and pool pedestrians based on their characteristics, capturing and integrating deep features of the group to generate the final prediction. The subsequent network learns pedestrian motion patterns and simulates their interactive relationships. The framework not only addresses the limitations of overly simplistic aggregation methods but also ensures reliable interactions with sparse directionality. Additionally, to evaluate the effectiveness of our model, we introduce a new evaluation metric termed collision prediction error, which incorporates map environment information to assess the comprehensiveness of multimodal prediction results. Experimental results on public pedestrian trajectory prediction benchmark demonstrate that our method outperforms the state-of-the-art methods.