Dynamic Graph Neural Network for Vehicle Trajectory Prediction and Driving Intent Recognition

To address the limitations of existing vehicle trajectory prediction methods, including insufficient modeling of dynamic inter-vehicle interactions, weak temporal continuity of complex driving intentions such as lane-changing, and high uncertainty in future trajectory prediction, this paper proposes a vehicle trajectory prediction method that integrates Dynamic Graph Neural Networks (DyGNN) with Transformer. Specifically, a time-varying interaction graph is constructed to model the dynamically evolving topological interaction relationships among vehicles, while a Transformer encoder is employed to extract temporal dependency features from historical trajectory sequences. In this way, the joint representation of spatial interaction information and temporal evolution information is achieved, thereby improving the accuracy and continuity of driving intention recognition in complex traffic scenarios. On this basis, driving intention is further introduced into the trajectory prediction process as a prior constraint, which effectively reduces the uncertainty of future trajectory prediction. Comparative experiments on real-world traffic datasets demonstrate that the proposed method maintains low prediction errors across different prediction horizons, showing good effectiveness and robustness.