Vehicle Trajectory Prediction Based on Spatial-Temporal Graph Attention Convolutional Network

Abstract

Vehicle trajectory prediction is a crucial technology in fields such as traffic management,intelligent-car,and autonomous driving.Accurately predicting vehicle trajectories contributes to safe driving.In urban traffic scenarios,the spatial- temporal features of vehicle trajectory data are complex and variable.To fully capture the dynamic spatial-temporal correlations in the data,enhance trajectory prediction accuracy,and simultaneously reduce model complexity,this paper proposes a spatial- temporal graph attention convolutional network(STGACN).It utilizes a trajectory information embedding module to transform historical vehicle trajectory data into spatial-temporal graphs.Subsequently,it extracts and combines temporal and spatial features of trajectory data through stacked spatial-temporal convolution blocks.Finally,encoding and decoding are performed by gated recurrent units to obtain the predicted trajectory.The model employs a gated convolutional network composed of dilated causal convolutions and gating units to extract temporal features,avoiding the redundant iterations introduced by recurrent neural network.The fusion of spatial- temporal features in the spatial-temporal convolution blocks group enables the model to focus on richer scene features.This results in a model with fewer parameters,faster trajectory prediction inference speed,and improved prediction accuracy.Experiments are conducted on real trajectory datasets,including Argoverse and NGSIM,and the results demonstrate that the proposed STGACN model exhibits higher prediction accuracy and efficiency than the compared baseline models.