To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.

Original languageEnglish
Article number106060
JournalOcean Engineering
Volume186
DOIs
Publication statusPublished - Aug 2019

    Research areas

  • C4.5 algorithm, Classification rule, Data mining, Decision-making, Intelligent ship, Ship maneuvering

ID: 55038962