LSTM-based graph attention network for vehicle trajectory prediction

Vehicle Trajectory Prediction (VTP) is one of the key technologies for autonomous driving, which can improve the safety and collaboration of the autonomous driving system. The interaction behavior among vehicles in reality has an impact on VTP. However, many methods ignore the interaction among vehicles, which results in limited accuracy of prediction results. Therefore, we propose a Long Short-Term Memory (LSTM)-based Graph Attention Network (GAT) method for VTP, which encodes vehicle trajectory information with LSTM networks and represents vehicle interactions with GAT. Firstly, in order to capture the temporal relationship between positions and consider their influence, we use LSTM model to encode the position data. Meanwhile, to comprehensively model vehicle motion and use multidimensional feature representation, we employ another LSTM model to encode the motion data, including position, velocity and acceleration. Secondly, to learn distinct feature representation, we use one GAT module to process the LSTM position encoding features for capturing spatial relationships of position information. Another GAT module is employed to process the LSTM motion encoding features for fully considering multidimensional motion dynamics and spatial–temporal dependencies. Finally, the LSTM decoder receives all features and predicts the vehicle trajectory. The experimental results show that the proposed method demonstrates superior predictive performance by using the Next Generation Simulation (NGSIM) dataset.