Influence of environmental factors on human-like decision-making for intelligent ship

Journal article (2019)

Authors

J. Xue Safety and Security Science - TPM , National Engineering Research Center for Water Transport Safety (WTSC), Ministry of Education Hangzhou, Wuhan University of Technology
Zhijun Chen National Engineering Research Center for Water Transport Safety (WTSC), Ministry of Education Hangzhou, Wuhan University of Technology
E. Papadimitriou Safety and Security Science - TPM
Chaozhong Wu Wuhan University of Technology, Ministry of Education Hangzhou, National Engineering Research Center for Water Transport Safety (WTSC)
P.H.A.J.M. van Gelder Safety and Security Science - TPM
Research Group
Safety and Security Science (TPM) (TU Delft)
Data mining Decision-making Ship maneuvering C4.5 algorithm Classification rule Intelligent ship
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Published Date

2019

Language

English

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Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Safety and Security Science

Abstract

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.