Multicolumn Self-Attention GRU Model for Intersection Vehicle Trajectory Prediction

Trajectory prediction plays a crucial and indispensable role in autonomous driving, enhancing safety and reliability, particularly in complex intersections with variable vehicle trajectories. This study aims to investigate the application of vehicle trajectory prediction in intersections and to improve the prediction accuracy of the model by utilizing multicolumn neural networks and self-attention mechanisms. Trajectory data were extracted using DataFromSky software from unmanned aerial vehicle (UAV)-captured aerial footage at specific intersections in Xi’an, Shaanxi Province, China. The deep learning model developed for trajectory prediction included a multicolumn (MC) neural network and a self-attention (SA) mechanism based on the gated recurrent unit (GRU). The model was abbreviated as MC-SA-GRU in this study. The multicolumn structure captures richer feature representations, whereas SA captures temporal correlations in time-series data. Experimental comparisons on corresponding data sets demonstrate the exceptional performance of these models in accurately predicting vehicle trajectories at intersections. This study contributes valuable insights for improving autonomous driving systems, advancing safety and reliability in real-world scenarios.