Multi-attribute decision-making method for prioritizing maritime traffic safety influencing factors of autonomous ships’ maneuvering decisions using grey and fuzzy theories

Journal article (2019)

Authors

J. Xue National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology, Safety and Security Science - TPM , Ministry of Education Hangzhou
P.H.A.J.M. van Gelder Safety and Security Science - TPM
G.L.L.M.E. Reniers Katholieke Universiteit Leuven, Safety and Security Science - TPM , Universiteit Antwerpen
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)
Research Group
Safety and Security Science (TPM) (TU Delft)
Decision-making Autonomous ships Fuzzy logic Quantitative assessment Grey relational analysis Maritime safety
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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

Ship maneuvering decisions are influenced by several factors, and it is essential to prioritize the main influencing factors for efficient selection of the corresponding maneuvering decisions. Meanwhile, the autonomous ship maneuvering decision-making influencing factors constitute a typical grey system, which is suitable for research by grey relational analysis. Furthermore, in the fuzzy approach, linguistic assessment of factors is evaluated to obtain priorities numbers. Therefore, this study mainly focuses on the concept of human-like maneuvering for autonomous ships. Based on experimental data of experienced seafarers and using a simulation platform under the scenario of the Shanghai Waigaoqiao wharf, an inference model utilizing grey and fuzzy theories is proposed. The proposed model combined with expert linguistic terms in order to select the ship maneuvering decision-making main influencing factors from multi-source influencing factors (in overall and separated categories of natural environment, ship motion, force parameters, draft, and position), and to study the decision-making prioritization for maritime traffic safety for specific ship maneuvering scenarios. This method can prioritize the main factors which affect maneuvering decisions as well as guide an autonomous ship-assisted or automatic maneuvering evaluation system for the research of human-like maneuvering behavior. This study provides a new perspective on the identification of main ship maneuvering decision-making influencing factors in theory and in practice. It can be utilized for better decision-making concerning maritime traffic safety of autonomous ship maneuvering, which in turn makes shipping safer and promote the application and spreading of autonomous ships.