Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction with Uncertainty Estimation
This work addresses the need for reliable uncertainty estimation in maritime surveillance, offering an incremental improvement over existing methods.
The paper tackles vessel trajectory prediction by extending deep learning frameworks to estimate prediction uncertainty via Bayesian modeling, showing that using additional information like ship intention improves both accuracy and uncertainty quantification.
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).