In current research on vehicle trajectory prediction,the existing Graph Attention Network(GAT),which is based on a static attention mechanism,fails to effectively capture interactions between vehicles in complex road conditions.To address this issue,this paper proposed an Encoder-Decoder Dynamic Graph Attention Network(ED-DGAT)to predict future trajectories of highway vehicles.In this model,the encoding module incorporates a dynamic graph attention mechanism to learn spatial interactions among vehicles.Simultaneously,a simplified dynamic graph attention network is adopted to model the interdependencies of vehicle movements during the decoding phase.This paper evaluated the proposed algorithm using the NGSIM dataset and conducted comparative analysis with other models such as LSTM,Social-LSTM(S-LSTM),and CS-LSTM.The results show that the Root Mean Squared Error(RMSE)of predicted trajectory has been reduced by 25％,and the inference speed is 2.61 times of the CS-LSTM model.
维普
万方数据