Abstract：The existing recurrent neural networks are subject to training overfitting, low prediction accuracy, poor
generalization ability, and weak adaptability in solving target trajectory prediction. A target trajectory prediction method
using an improved attention mechanism and Gated Recurrent Unit (GRU) was proposed, which could automatically
terminate the network training process through an early stopping method to prevent overfitting during training. It saved
the optimal network parameters during network training through the model checkpoint function. By introducing an
attention mechanism into the GRU network and assigning different weights to trajectory features to focus on key
trajectory information, the predictive performance of the network was optimized Finally, simulation experiments
results show that the proposed method effectively improves the prediction accuracy, generalization, and adaptability of
recurrent neural networks.
