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Probabilistic Maritime Trajectory Prediction in Complex Scenarios Using Deep Learning.

Kristian Aalling Sørensen1, Peder Heiselberg2, Henning Heiselberg1

  • 1DTU Security, National Space Institute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.

Sensors (Basel, Switzerland)
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

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Predicting ship locations is crucial for maritime safety. A new deep learning model, the Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN), offers probabilistic future trajectory predictions, improving maritime surveillance and identifying "dark ships".

Area of Science:

  • Maritime Technology
  • Artificial Intelligence
  • Oceanography

Background:

  • Increasing maritime activity necessitates enhanced surveillance and safety measures.
  • Automatic Identification System (AIS) data aids in tracking, but 'dark ships' pose a surveillance challenge.
  • Predicting ship trajectories is complex due to numerous possible routes and inherent uncertainty.

Purpose of the Study:

  • To develop a probabilistic deep learning model for predicting future ship locations.
  • To improve maritime surveillance by characterizing ship trajectory distributions.
  • To aid in identifying 'dark ships' by predicting probable future positions.

Main Methods:

  • Implementation of a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model.
Keywords:
Automatic Identification System (AIS)Long Short Term Memory (LSTM)Maritime Situational Awareness (MSA)Mixture Density Network (MDN)deep learningtrajectory prediction

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  • Utilizing AIS data from 3631 cargo ships in a region west of Norway.
  • Characterizing conditional probability using an 11-dimensional Gaussian distribution for probabilistic trajectory prediction.

    • Main Results:

    • The BLSTM-MDN model achieved a test Negative Log Likelihood loss of -9.96, with a mean distance error of 2.53 km at 50 minutes into the future.
    • The model successfully predicted multiple probable trajectories from a single input trajectory.
    • Performance was comparable to deterministic deep learning models on straight paths but superior in complex navigational scenarios.

    Conclusions:

    • The BLSTM-MDN provides a robust method for probabilistic ship trajectory prediction, outperforming deterministic models in complex situations.
    • This approach enhances maritime safety and surveillance capabilities, particularly for identifying unknown or 'dark' vessels.
    • Probabilistic predictions offer a more realistic representation of future ship movements compared to single deterministic forecasts.