Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data

This paper presents Transformer and Performer models for forecasting surface vessel trajectories in confined waters using AIS position data. It introduces positional heat maps generated via Kernel Density Estimation and encoded with a CNN to incorporate local vessel movement patterns. The models, trained with quantile loss, achieve superior performance over prior work and provide prediction intervals essential for collision avoidance.

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