Research on Multi-Task Spatio-Temporal Learning Model with Dynamic Graph Attention for Joint Pedestrian Trajectory and Intention Prediction

Accurate pedestrian trajectory prediction and intention estimation are crucial for autonomous systems and intelligent transportation applications. However, existing methods often address these two highly correlated tasks in isolation and rely on static or heuristic interaction modeling, leading to insufficient adaptability and limited generalization capability in dynamic traffic scenarios. To this end, this paper proposes MTG-TPNet, a Multi-task dynamic Graph Transformer network for joint Trajectory Prediction and intention estimation. The research framework integrates three key innovations: First, a dynamic graph neural network enhanced with motion features, whose graph topology can be adaptively learned end-to-end based on semantic and motion contexts to accurately capture evolving interactions. Second, a multi-granularity attention mechanism that collaboratively fuses geometric proximity, semantic similarity, and physical hard constraints to achieve fine-grained modeling of spatiotemporal dependencies. Third, a dynamic correlation loss based on Bayesian uncertainty, which balances multi-task learning in an adaptive manner and encourages beneficial interactions across tasks. Extensive experiments on the publicly available PIE and ETH/UCY datasets demonstrate that MTG-TPNet achieves state-of-the-art performance. On the PIE dataset, the proposed model significantly outperforms the best baseline model in trajectory prediction metrics, achieving an Average Displacement Error (ADE) of 0.21 and a Final Displacement Error (FDE) of 0.29. This represents a 27.6% reduction in ADE while maintaining stability in intention estimation. Systematic ablation studies validate the effectiveness of each proposed module, with the model retaining an average performance of 69.3%. Furthermore, cross-dataset evaluations confirm its superior generalization capability. This study provides a powerful unified framework for robust pedestrian behavior understanding in complex urban traffic scenarios.