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

• URL: http://arxiv.org/abs/2606.06311v1
• Date: Thu, 04 Jun 2026 15:52:21 GMT
• Title: AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks
• Abstract summary: This paper presents an empirical investigation of memory-based trajectory prediction using Automatic Identification System (AIS) data.<n> Experiments on data from the Gulf of Mexico and the New York Bight demonstrate consistent and substantial performance gains.
• Score: 8.717253904965371
• License: http://creativecommons.org/licenses/by/4.0/
• 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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