Social-TMAGRU: Pedestrian Trajectory Prediction via Temporal Social Attention in Crowded Scenes

Accurate prediction of pedestrian trajectories is essential for safety in autonomous driving. Pedestrian movements are shaped by individual behaviors, interactions with surrounding agents, and implicit social relationships. The primary challenge lies in effectively modeling these intricate social interactions while integrating temporal dependencies. Existing approaches typically aggregate agent states but often fail to account for hidden social relationships and trajectory multimodality. To address these challenges, this article proposes the social temporal multihead attention gated recurrent unit (Social-TMAGRU), a model that integrates two key submodules: social multihead attention (S-MHA) and temporal social attention GRU (TSA-GRU). The S-MHA submodule captures implicit social relationships between pedestrians by modeling their latent interactions, while TSA-GRU handles temporal dependencies and evolving dynamics in pedestrian trajectories. Experimental evaluations of multiple public datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art methods in predictive accuracy and computational efficiency. The model excels in complex and high-density environments, demonstrating robust performance in dynamically changing scenarios, making it highly suitable for real-world autonomous driving applications.