Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction With Uncertainty Estimation

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 article 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 aleatoric and epistemic 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).

Automated summary

Recent deep learning methods can accurately predict future vessel positions using historical AIS data, but quantifying prediction uncertainty is crucial in maritime surveillance. This article explores how recurrent encoder-decoder neural networks can not only predict but also yield a corresponding prediction uncertainty by incorporating Bayesian modeling of uncertainties.