Trajectory Prediction Model for Autonomous Vehicles in Highway Scenarios Based on Spatiotemporal Interaction Feature Extraction

Accurately predicting the future trajectories of surrounding vehicles is one of the core tasks in autonomous driving, and its precision is directly related to the safety and reliability of decision-making, path planning, and control execution. However, challenges such as the complexity of traffic participants’ behaviors, the variability of interactions, and the highly dynamic nature of traffic environments make it difficult for existing methods to effectively model spatiotemporal dependencies and achieve accurate long-term prediction in dynamic scenarios, thus limiting their applicability in real-world settings. In this paper, we propose a Transformer-based trajectory prediction model with a spatiotemporal attention mechanism to extract and effectively model vehicle motion and spatial interactions. Specifically, the temporal attention module captures the motion patterns of the target vehicle across the time dimension, while the spatial attention module constructs vehicle interactions through an adjacency graph, characterizing local interaction features such as relative positions and lane relationships. In addition, a GRU structure is incorporated as a complementary temporal component to enhance the model’s ability to capture continuous trajectory evolution trends. We evaluate the proposed model on the public NGSIM dataset, and the results demonstrate that our approach significantly outperforms a variety of existing trajectory prediction methods. In particular, the integration of spatiotemporal interaction feature extraction plays a crucial role in improving prediction accuracy, especially for long-term trajectory prediction, where it substantially enhances the model’s ability to represent complex and dynamic traffic scenarios and generates more accurate trajectories. Finally, ablation studies are conducted to analyze the importance of each module, verifying the key roles of the spatiotemporal attention mechanism and the GRU structure in modeling complex interactive behaviors and improving predictive performance.