DSTIFormer: Vehicle Trajectory Prediction Considering Dynamic Spatial-Temporal Interaction-Aware With Transformer Network

Current vehicle trajectory prediction methods often fail to capture the complex spatial and temporal interactions among surrounding vehicles in dynamic environments, leading to suboptimal prediction accuracy. In response to these challenges, we propose a novel Transformer-based model for vehicle trajectory prediction, denoted as the Dynamic Spatial-Temporal Interaction-aware Transformer Network (DSTIFormer). The three main parts of the DSTIFormer architecture are a spatial-temporal embedding module, a spatial-temporal dynamics interaction-aware module, and a trajectory encoderdecoder module. The spatial-temporal embedding module aims to encode vehicular trajectory interactions into latent representations that simultaneously preserve spatial dependencies and temporal features. The second module utilizes a temporal transformer network (TTN) along with a spatial graph transformer network (STN) to capture the dynamic spatial-temporal interactions between vehicles. To make trajectory predictions more accurate, the third module uses a sequence-to-sequence (Seq2Seq) architecture as the encoder and an attention-based decoder layer to effectively extract the long-range sequential dependencies in the process. The model's prediction accuracy with the root mean squared error (RMSE) metric on two publicly available datasets: NGSIM and HighD, and the results demonstrate that our proposed model outperforms several benchmark methods in long-term prediction.