Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)
Abstract: The prediction of ship trajectories is a growing field of study in artificial intelligence. Traditional methods rely on the use of LSTM, GRU networks, and even Transformer architectures for the prediction of spatio-temporal series. This study proposes a viable alternative for predicting these trajectories using only GNSS positions. It considers this spatio-temporal problem as a natural language processing problem. The latitude/longitude coordinates of AIS messages are transformed into cell identifiers using the H3 index. Thanks to the pseudo-octal representation, it becomes easier for language models to learn the spatial hierarchy of the H3 index. The method is qualitatively compared to a classical Kalman filter and quantitatively to Seq2Seq and TrAISformer models. The Fréchet distance is introduced as the main evaluation metric for these comparisons. We show that it is possible to predict ship trajectories quite precisely up to 8 hours ahead with 30 minutes of context, using solely GNSS positions, without relying on any additional information such as speed, course, or external conditions — unlike many traditional methods. We demonstrate that this alternative works well enough to predict trajectories worldwide.
Submission Length: Long submission (more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=tIfS6jyO9f
Changes Since Last Submission: - **Title of table 3 Updated**: The title has been changed to make it clearer we evaluated our models on a public dataset.
- **Data Availability Section Updated**: Links to the public AIS dataset of the Danish Maritime Authority have been added to the Data Availability section (page 19).
Assigned Action Editor: ~Dit-Yan_Yeung2
Submission Number: 3729
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