For dynamic scenarios involving interaction among multiple vehicles,intelligent vehicles should be able to predict the future trajectories of surrounding vehicles for safe and efficient driving.This paper proposes a trajectory prediction method that considers dynamic interactions among vehicles.First,based on the historical trajectory information of the target and surrounding vehicles,a dynamic spatio-temporal correlation graph is constructed as the input for the interaction feature extraction module.The graph attention mechanism is then used to capture the temporally varying interaction feature parameters.Second,the historical temporal information of the target vehicle is fused with the variable interaction feature parameters.A context vector is obtained by an LSTM encoder embedded with a temporal attention mechanism,followed by using the LSTM decoder to output the future trajectory of the target vehicle.Finally,the proposed model is trained and validated on the CitySim dataset,and transfer experiments are conducted using the CQSkyEye dataset.The results show that the model achieves an RMSE of 0.82 m in a 5 s prediction horizon,demonstrating a 15%improvement in accuracy compared to other popular models.The model also demonstrates the ability to make predictions with less than 2 s lead time.In terms of transferability,the proposed model outperforms others with an RMSE of 6.43 m in the 5 s prediction horizon after adjusting the distance threshold parameter for graph construction,showing an improvement of over 48%in transfer prediction capability.
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