Vehicle Trajectory Prediction Using Hierarchical LSTM and Graph Attention Network

Abstract

Vehicle trajectory prediction (VTP) is an important task that can enhance the safety and efficiency of autonomous driving. However, existing VTP methods often struggle to fully extract spatiotemporal features, resulting in inaccurate prediction results. To solve this problem, we propose a hierarchical long short-term memory and graph attention network (HLSTM-GAT) model. First, we design a hierarchical network architecture to model different spatiotemporal features. The first-layer network (FLN) focuses on short-term trajectory information and immediate vehicle interactions to generate preliminary candidate trajectories. In the FLN, long short-term memory (LSTM) encoder processes historical trajectories to extract temporal features of vehicles, while a graph attention network (GAT) handles the LSTM-encoded outputs to capture spatial features between vehicles. Second, the second-layer network (SLN) combines the candidate trajectories generated by the FLN with historical trajectories to form comprehensive trajectories for accurate prediction. Subsequently, SLN adopts a GAT to process these comprehensive trajectories to precisely model the spatial relationships between vehicles. Moreover, we propose two distance threshold-based dynamic GAT models to perfectly capture spatial features between vehicles. They construct vehicle spatial interaction relationships at two distinct levels based on real-time distances in the FLN and predicted future distances in the SLN, respectively. These dynamic GAT models effectively filter out irrelevant information and enable the proposed method to consider potential future interactions. Finally, we conduct extensive experiments on two publicly available datasets, and experimental results demonstrate that the proposed method outperforms other state-of-the-art VTP methods in terms of prediction accuracy, robustness and computational efficiency.