  Recent deep learning methods for vessel trajectory prediction are able to
learn complex maritime patterns from historical Automatic Identification System
(AIS) data and accurately predict sequences of future vessel positions with a
prediction horizon of several hours. However, in maritime surveillance
applications, reliably quantifying the prediction uncertainty can be as
important as obtaining high accuracy. This paper extends deep learning
frameworks for trajectory prediction tasks by exploring how recurrent
encoder-decoder neural networks can be tasked not only to predict but also to
yield a corresponding prediction uncertainty via Bayesian modeling of epistemic
and aleatoric uncertainties. We compare the prediction performance of two
different models based on labeled or unlabeled input data to highlight how
uncertainty quantification and accuracy can be improved by using, if available,
additional information on the intention of the ship (e.g., its planned
destination).
