AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks
Wonmo Koo, Sanha Chang, Heeyoung Kim
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.