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

Wonmo Koo; Sanha Chang; Heeyoung Kim

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arXiv · 2606.06311

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

Wonmo Koo·Sanha Chang·Heeyoung Kim

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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.

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BibTeX RIS

Wonmo Koo, Sanha Chang, Heeyoung Kim. 2026-06-04. AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks. https://arxiv.org/abs/2606.06311

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