Deep learning based ship trajectory prediction with AIS

Jian Guan; Jun Liu; Qianqian Mo; Qilin Yang

doi:10.1117/12.3026330

27 March 2024 Deep learning based ship trajectory prediction with AIS

Jian Guan, Jun Liu, Qianqian Mo, Qilin Yang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051F (2024) https://doi.org/10.1117/12.3026330
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

It is crucial to monitor and predict the trajectory of ships for preventing maritime accidents and enhancing maritime efficiency. Traditional data mining algorithms face serious challenges in terms of computational complexity and generalization capability. To address these challenges, a deep learning-based ship trajectory prediction method is proposed in this paper. By performing clustering on trajectory data, similar trajectories are grouped into clusters, effectively reducing the complexity of the data. Subsequently, the Seq2Seq model is employed to model and predict trajectories within each cluster. Experimental results demonstrates effectiveness and superiority of the proposed method in ship motion trajectory prediction tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian Guan, Jun Liu, Qianqian Mo, and Qilin Yang "Deep learning based ship trajectory prediction with AIS", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051F (27 March 2024); https://doi.org/10.1117/12.3026330

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