  Data-driven methods open up unprecedented possibilities for maritime
surveillance using Automatic Identification System (AIS) data. In this work, we
explore deep learning strategies using historical AIS observations to address
the problem of predicting future vessel trajectories with a prediction horizon
of several hours. We propose novel sequence-to-sequence vessel trajectory
prediction models based on encoder-decoder recurrent neural networks (RNNs)
that are trained on historical trajectory data to predict future trajectory
samples given previous observations. The proposed architecture combines Long
Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data
and generate future predictions with different intermediate aggregation layers
to capture space-time dependencies in sequential data. Experimental results on
vessel trajectories from an AIS dataset made freely available by the Danish
Maritime Authority show the effectiveness of deep-learning methods for
trajectory prediction based on sequence-to-sequence neural networks, which
achieve better performance than baseline approaches based on linear regression
or feed-forward networks. The comparative evaluation of results shows: i) the
superiority of attention pooling over static pooling for the specific
application, and ii) the remarkable performance improvement that can be
obtained with labeled trajectories, i.e. when predictions are conditioned on a
low-level context representation encoded from the sequence of past
observations, as well as on additional inputs (e.g., the port of departure or
arrival) about the vessel's high-level intention which may be available from
AIS.
