Name
Title | ||
|---|---|---|
Deep Learning Methods for Vessel Trajectory Prediction Based on Recurrent Neural Networks |
Abstract | ||
|---|---|---|
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 ba... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TAES.2021.3096873 | IEEE Transactions on Aerospace and Electronic Systems |
Keywords | DocType | Volume |
Trajectory,Artificial intelligence,Predictive models,Marine vehicles,Data models,Recurrent neural networks,Safety | Journal | 57 |
Issue | ISSN | Citations |
6 | 0018-9251 | 9 |
PageRank | References | Authors |
0.58 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuele Capobianco | 1 | 9 | 0.58 |
| Leonardo M. Millefiori | 2 | 39 | 8.46 |
| Nicola Forti | 3 | 21 | 4.91 |
| Paolo Braca | 4 | 467 | 46.44 |
| Peter Willett | 5 | 10 | 1.61 |