Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)

TL;DR

This paper introduces a novel approach to maritime trajectory forecasting by transforming GNSS data into a language modeling problem using H3 index, achieving accurate predictions up to 8 hours ahead without additional data.

Contribution

It presents a new method that models ship trajectories as a language task using H3 index and causal language models, outperforming traditional Kalman filter approaches.

Findings

Accurately predicts ship trajectories up to 8 hours ahead.

Uses only GNSS positions without extra data.

Performs well worldwide in maritime scenarios.

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 compared with a classical Kalman filter, widely used in the maritime domain, and introduces the Fr\'echet distance as the main evaluation metric. 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.