Pedestrian Trajectory Prediction Method Based on Graph Attention Interaction

Pedestrian trajectory prediction has made significant research progress in the fields of autonomous driving and intelligent transportation.Due to the dual influence of individual and environmental factors,pedestrian trajectories exhibit uncertainty and complexity.Accurately generating multimodal trajectories by leveraging the interactive features of trajectory data remains a challenge.One of the primary challenges in this field is the accurate modeling of spatial temporal interactions among pedestrians.To address the complexity of pedestrian spatial temporal interactions,this paper proposes a spatial temporal graph neural network based on graph attention.The proposed method quantitatively represents the spatial interactions between pedestrians,focusing on key interactions,and represents pedestrian trajectory information as a directed spatial temporal graph.The spatial position features and interaction features are extracted using a graph attention mechanism,while the temporal features are obtained using a self-attention mechanism.By integrating spatial temporal feature information,the model generates multimodal future trajectories based on historical trajectory data and interaction information.Experiments conducted on the publicly available ETH-UCY dataset demonstrate that the proposed method outperforms the baseline models,achieving improvements of 3.4% and 2.1% in ADE and FDE,respectively.Additionally,the proposed model has a shorter inference time,ensuring real-time inference responses.Visualization results further indicate that the predicted pedestrian trajectories are plausible and socially acceptable,demonstrating promising prospects for engineering applications.