Interaction-Aware Vehicle Trajectory Imputation via Scalable Graph Transformer with Spatiotemporal Semantic

The widespread deployment of roadside sensors in cooperative vehicle-infrastructure systems enables largescale vehicle trajectory data collection, crucial for trajectory planning and driving safety analysis of autonomous vehicles. However, sensor failure and environmental interference often lead to significant data gaps. To address this issue, we propose a Graph Transformer-based Trajectory Imputation (GTTI) model, which utilizes a Scalable Graph Transformer (SGT) to reconstruct missing trajectories by capturing multivehicle interactions and integrating spatiotemporal semantics of driving scenarios. Combining attention mechanisms and semantic understanding, the GTTI effectively handles largescale imputation, even in complex traffic scenarios with substantial data loss. Extensive evaluations using real-world highway data were carried out. The results show that GTTI outperforms state-of-the-art models, improving imputation accuracy by 12.7% in Average Displacement Error (ADE) and 8.8% in Final Displacement Error (FDE).