Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management

Wenjun Zhang,Weiliang Qiao,Yingjun Zhang

doi:10.3390/su14042103

Abstract

To identify and screen the risk scenarios for the navigation risk of intelligent ships, the analysis and evaluation of navigational risks were performed in this study. Risk scenarios were developed and evaluated by mapping the hierarchical holographic modeling (HHM) into risk filtering, ranking and management (RFRM). In detail, considering the insignificant influences of some factors on navigational activities, risk factors were filtered and ranked using the RFRM model. Seven final factors were successfully determined, including traffic flow, navigation environment understanding, ship–shore interaction capabilities, target recognition capabilities, communication equipment reliabilities, professional skills, and situation judgments. The results indicated that cargo security can be guaranteed by following navigational risk identification and screening steps, and thus our findings provide theoretical guidance for the dynamic management of maritime organizations and ship companies. In addition, the proposed methodology is desirable for making predictions on maritime traffic risks.

Highlights

The rapid development of artificial intelligence in the maritime industry has promoted the probability of operating ocean-going intelligent ships
According to documents issued by the Maritime Safety Committee (MSC) affiliated with the International Maritime Organization (IMO) [1,2,3], it can be reasonably speculated that intelligent ships would play an important role in the sustainable development of the maritime industry
The safety issues associated with intelligent ships challenge their application, which has to be addressed for the sustainable development of artificial intelligence in the maritime industry

Summary

Introduction

The rapid development of artificial intelligence in the maritime industry has promoted the probability of operating ocean-going intelligent ships. According to documents issued by the Maritime Safety Committee (MSC) affiliated with the International Maritime Organization (IMO) [1,2,3], it can be reasonably speculated that intelligent ships would play an important role in the sustainable development of the maritime industry. The safety issues associated with intelligent ships challenge their application, which has to be addressed for the sustainable development of artificial intelligence in the maritime industry. The USV system is widely studied and applied, corresponding to the mentioned third and fourth intelligence levels With this background, navigational risk identification and screening of intelligent ships have been researched in the present study. Extensive studies associated with navigational risk identification and screening of intelligent ships are presented both theoretically and practically [9]. According to more than 100 reports of ship accidents, the MASS is selected as the accident objective, and the accident conditions are simulated

Objectives

Results

Conclusion