Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm

Zhuang, ChangXi; Chen, Chao

doi:10.3389/fnbot.2022.1049343

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…To identify the areas with the densest ship trajectories, we used an algorithm called clustering analysis to process all ship trajectory data [20]. Cluster analysis is an important algorithm for analyzing vessel trajectories.…”

Section: Ais Datasetmentioning

confidence: 99%

“…All models utilized in this experiment were specifically designed for time series forecasting of ship trajectories. Seven different models were used: Autoformer [23], Informer [24], Transformer [25], Linear, NLinear, DLinear, and PatchTST [20]. The input consisted of a time series of length seq_len, which contained both multivariate and univariate feature data.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Large-Scale Long-Term Prediction of Ship AIS Tracks via Linear Networks with a Look-Back Window Decomposition Scheme of Time Features

Zhao,

Wang,

Gao

et al. 2023

JMSE

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Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas.

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Section: Ais Datasetmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%