Blar i forfatter "Murray, Brian"

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    • An AIS-based deep learning framework for regional ship behavior prediction 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-27)
      This study presents a deep learning framework to support regional ship behavior prediction using historical AIS data. The framework is meant to aid in proactive collision avoidance, in order to enhance the safety of maritime transportation systems. In this study, it is suggested to decompose the historical ship behavior in a given geographical region into clusters. Each cluster will contain trajectories ...

    • An AIS-Based Multiple Trajectory Prediction Approach for Collision Avoidance in Future Vessels 

      Murray, Brian; Perera, Lokukaluge Prasad (Peer reviewed; Bok; Chapter, 2019-11-11)
      This study presents a method to predict the future trajectory of a target vessel using historical AIS data. The purpose of such a prediction is to aid in collision avoidance in future vessels. The method presented in this study extracts all trajectories present in an initial cluster centered about a vessel position. Features for each trajectory are then generated using Principle Component Analysis ...

    • A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-21)
      Advances in artificial intelligence are driving the development of intelligent transportation systems, with the purpose of enhancing the safety and efficiency of such systems. One of the most important aspects of maritime safety is effective collision avoidance. In this study, a novel dual linear autoencoder approach is suggested to predict the future trajectory of a selected vessel. Such predictions ...

    • Machine Learning for Enhanced Maritime Situation Awareness: Leveraging Historical AIS Data for Ship Trajectory Prediction 

      Murray, Brian (Doctoral thesis; Doktorgradsavhandling, 2021-05-03)
      In this thesis, methods to support high level situation awareness in ship navigators through appropriate automation are investigated. Situation awareness relates to the perception of the environment (level 1), comprehension of the situation (level 2), and projection of future dynamics (level 3). Ship navigators likely conduct mental simulations of future ship traffic (level 3 projections), that ...

    • Proactive Collision Avoidance for Autonomous Ships: Leveraging Machine Learning to Emulate Situation Awareness 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Autonomous ship technology is developing at a rapid pace, with the aim of facilitating safe ship operations. Collision avoidance is one of the most critical tasks that autonomous ships must handle. To support the level of safety associated with collision avoidance, this study suggests to provide autonomous ships with the ability to conduct proactive collision avoidance maneuvers. Proactive collision ...

    • Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-20)
      This study presents a method in which historical AIS data are used to predict the future trajectory of a selected vessel. This is facilitated via a system intelligence-based approach that can be subsequently utilized to provide enhanced situation awareness to navigators and future autonomous ships, aiding proactive collision avoidance. By evaluating the historical ship behavior in a given geographical ...