Vehicle Trajectory Prediction Strategy Based on Spatio-Temporal Graph Attention for Complex Intersection Scenarios

The development of the low-altitude economy presents abundant opportunities for data acquisition in Intelligent Transportation Systems (ITS). High-density interactions among heterogeneous traffic participants at complex urban intersections pose significant challenges for autonomous vehicle trajectory prediction. Existing Graph Neural Network (GNN) models often struggle to adequately capture interaction variances and global spatial dependencies among these heterogeneous agents. To address this, we propose STGANet, a Spatial-Temporal Graph Attention Network integrating Transformer-based multi-head attention with graph convolutions. STGANet employs a local-global feature extraction strategy: a dynamic multi-group GCN first extracts multi-semantic local spatial information, while a temporal attention mechanism captures long-term sequential dependencies. A feature fusion gate then adaptively integrates these spatio-temporal features. Subsequently, a global spatial multi-head attention module is applied to thoroughly mine the overarching heterogeneous interaction dynamics. Evaluated on the real-world ApolloScape trajectory dataset, STGANet demonstrates superior predictive performance in heterogeneous traffic flows. Compared to the baseline GRIP++ model, it reduces the Weighted Sum Average Displacement Error (WSADE) and Weighted Sum Final Displacement Error (WSFDE) by approximately 9% and 19%. Furthermore, with an average inference time of 12.8 ms, STGANet achieves high precision while strictly satisfying the real-time latency constraint (< 20 ms) required for practical autonomous driving applications.