STGANT: Spatiotemporal Generative Attention Network with Adversarial Training for Intersection Vehicle Trajectory Prediction

Abstract

Vehicle trajectories at urban road intersections are influenced by diverse spatiotemporal factors, with significant variations across different locations. To address these complexities, real-world trajectory data were collected from intersections in Xi’an, Shaanxi Province, China, and raw trajectories were extracted using the You Only Look Once v8 and BoT-SORT algorithms. This study presents the Spatiotemporal Generative Attention Network (STGANT), a novel generative adversarial network-based framework designed specifically for vehicle trajectory prediction at intersections. The STGANT integrates innovative mechanisms, such as agent attention, social pooling, and Long Short-Term Memory, capturing spatial and temporal dependencies among vehicles. By incorporating an improved attention module, STGANT achieves a balance between accuracy and computational efficiency, enabling region-specific training while reducing overall computational load. Comprehensive experiments demonstrate that the STGANT outperforms existing models for prediction accuracy, computational complexity, and parameter efficiency. Qualitative analysis further verifies that the STGANT effectively captures the intricate spatiotemporal dynamics of vehicle interactions, resulting in reliable and accurate trajectory predictions. This study contributes to the enhancement of safety and operational efficiency in intelligent transportation systems at urban intersections.