{"ID":3083643,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T03:05:32.813677833Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.06311","arxiv_id":"2606.06311","title":"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.","short_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 r...","url_abs":"https://arxiv.org/abs/2606.06311","url_pdf":"https://arxiv.org/pdf/2606.06311v1","authors":"[\"Wonmo Koo\",\"Sanha Chang\",\"Heeyoung Kim\"]","published":"2026-06-04T15:52:21Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}