Pedestrian trajectory prediction with high-order interactions

To tackle the problem of human trajectory prediction in complex scenes, we propose a model using hypergraph convolutional neural networks for social interaction (HGCNSI). Our model leverages a hypergraph structure to capture both high-order interactions and complex social dynamics among pedestrians (who often influence each other in a nonlinear and structured manner). First, we propose a social interaction module that improves the accuracy of interaction modeling by distinguishing between interacting and non-interacting pedestrians. Then, the hypergraph structure that can capture the complex and nonlinear relations among multiple pedestrians from the social interaction module is constructed. Furthermore, we exploit an improved attention mechanism called scene-coordinates attention that fuses the spatial and temporal features and models the unique historical movement information of each person. Finally, we introduce the SIRF module that filters the trajectories within one iteration to reduce the computational complexity and improve the prediction performance. We evaluate the proposed HGCNSI model on five publicly available datasets and demonstrate that it achieves state-of-the-art results. Specifically, the experiments show that our model outperforms existing methods in terms of prediction accuracy, using evaluation metrics, such as the average displacement error and the final displacement error.