Ship trajectory prediction method incorporating concatenated attention mechanism
WU Yue-gao
YU Wan-neng
ZENG Guang-miao
SHANG Yi-fan
LIAO Wei-qiang
Abstract：Ship trajectory prediction plays a crucial role in ensuring the navigation safety of ships.In order to enhance the accuracy of predicting future ship trajectories,this paper introduces a novel ship trajectory prediction method that combines the concatenation attention mechanism in Seq2Seq-CA with model predictive control(MPC).The incorporation of the concatenation attention mechanism within Seq2Seq enhances the model's understanding of sequence features.To further improve the accuracy and motion coherence of trajectory predictions,MPC is employed to correct the probability distribution of predicted trajectories,yielding the final output trajectories.During the training and testing processes,random initialization of sequence starting positions and the utilization of a sliding window approach are employed to enhance the utilization of sequence data.Performance testing on the AIS dataset demonstrates that Seq2Seq-CA improves prediction accuracy by 17.2％compared to the original Seq2Seq.After trajectory correction using MPC,Seq2Seq-CA exhibits a 39.9％increase in prediction accuracy and a 9.2％improvement in robustness.Qualitative analysis confirms that the proposed prediction method accurately and reasonably predicts future ship trajectories under various ship motion patterns.
Keywords：attention mechanismAIS datadeep learningtrajectory predictionmodel predictive control
Publication Date：2025-09-30
Online Publishing Date：2025-10-28(First online date of this platform, not the publication date of the document)
Pages：9( 1798-1806 )