Vehicle Trajectory Prediction Based on Spatial-Temporal Graph Attention Convolutional Network
Vehicle trajectory prediction is a crucial technology in fields such as traffic management,intelligent-car,and autono-mous 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 da-ta 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 ga-ting 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.Ex-periments are conducted on real trajectory datasets,including Argoverse and NGSIM,and the results demonstrate that the pro-posed STGACN model exhibits higher prediction accuracy and efficiency than the compared baseline models.
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