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A Data-Driven Collision Avoidable Area Estimation Model for MASS

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2025, 11(5), 28
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : October 8, 2025
  • Accepted : October 21, 2025
  • Published : October 31, 2025

Tae-min Hwang 1 KIM SUNG CHEOL 2

1국립목포해양대학교 대학원
2국립목포해양대학교

Accredited

ABSTRACT

Autonomous ships primarily navigate via autonomous navigation systems, with remote operators intervening in critical situations. Such interventions typically occur in urgent collision avoidance situation, where understanding the ship's maneuvering capabilities is essential. However, in a remote operation involving multiple ships, ascertaining the precise maneuvering range of a single ship is challenging. Hence, research using maneuvering data employs ship models. However, the model-based approach is required to apply each environment factor separately and to process heavy computing. This study proposes a model to estimate the collision avoidable area based on the ship's recent operational data. The methodology involves deriving the minimum turning circle and estimating ship maneuvering parameters. The proposed model is expected to help prevent collisions during urgent remote operations by providing an accurate estimation of the feasible collision avoidable area.

Citation status

* References for papers published after 2024 are currently being built.