AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks

Abstract:Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization. Although memory-augmented neural networks have recently shown strong performance in pedestrian and road-vehicle trajectory prediction by selectively retrieving relevant information from an external memory, their potential for vessel trajectory prediction remains underexplored. This paper presents an empirical investigation of memory-based trajectory prediction using Automatic Identification System (AIS) data. Experiments on data from the Gulf of Mexico and the New York Bight demonstrate consistent and substantial performance gains over a range of deep learning baselines that do not incorporate an external memory.

Artificial Intelligence (cs.AI)

arXiv:2606.06311 [cs.AI]

(or arXiv:2606.06311v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.06311 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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