Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation

The aim of this study is to construct an unmanned ship swarms monitoring model to improve autonomous decision-making efficiency and safety performance of unmanned ship navigation. A framework is proposed to determine the relationship between on-board decision-making and shore side monitoring, the pr...

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Main Authors: Ruolan Zhang, Masao Furusho
Format: Article
Language:English
Published: Gdynia Maritime University 2017-09-01
Series:TransNav: International Journal on Marine Navigation and Safety of Sea Transportation
Subjects:
Online Access:http://www.transnav.eu/files/Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation,748.pdf
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spelling doaj-6f83ed248c4d49b78bc639e8779ccb092020-11-25T01:34:21ZengGdynia Maritime UniversityTransNav: International Journal on Marine Navigation and Safety of Sea Transportation2083-64732083-64812017-09-0111346346810.12716/1001.11.03.11748Conversion Timing of Seafarer’s Decision-making for Unmanned Ship NavigationRuolan ZhangMasao FurushoThe aim of this study is to construct an unmanned ship swarms monitoring model to improve autonomous decision-making efficiency and safety performance of unmanned ship navigation. A framework is proposed to determine the relationship between on-board decision-making and shore side monitoring, the process of ship data detection, tracking, analysis and loss, and the application of decision-making algorithm, to discuss the different risk responses of specific unmanned ship types under various latent hazard environments, particularly in terms of precise conversion timing in switching over to remote control and full manual monitoring, to ensure safe navigation when the capability of automatic risk response inadequate. This frame-work makes it easier to train data and the adjustment for machine learning based on Bayesian risk prediction. It can be concluded that the automation level can be increased and the workload of shore-based seafarers can be reduced easily.http://www.transnav.eu/files/Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation,748.pdfMaritime SafetyUnmanned ShipUnmanned Ship NavigationOn-board Decision-MakingDecision-Making AlgorithmConversion TimingBayesian Risk PredictionSeafarers
collection DOAJ
language English
format Article
sources DOAJ
author Ruolan Zhang
Masao Furusho
spellingShingle Ruolan Zhang
Masao Furusho
Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
TransNav: International Journal on Marine Navigation and Safety of Sea Transportation
Maritime Safety
Unmanned Ship
Unmanned Ship Navigation
On-board Decision-Making
Decision-Making Algorithm
Conversion Timing
Bayesian Risk Prediction
Seafarers
author_facet Ruolan Zhang
Masao Furusho
author_sort Ruolan Zhang
title Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
title_short Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
title_full Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
title_fullStr Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
title_full_unstemmed Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation
title_sort conversion timing of seafarer’s decision-making for unmanned ship navigation
publisher Gdynia Maritime University
series TransNav: International Journal on Marine Navigation and Safety of Sea Transportation
issn 2083-6473
2083-6481
publishDate 2017-09-01
description The aim of this study is to construct an unmanned ship swarms monitoring model to improve autonomous decision-making efficiency and safety performance of unmanned ship navigation. A framework is proposed to determine the relationship between on-board decision-making and shore side monitoring, the process of ship data detection, tracking, analysis and loss, and the application of decision-making algorithm, to discuss the different risk responses of specific unmanned ship types under various latent hazard environments, particularly in terms of precise conversion timing in switching over to remote control and full manual monitoring, to ensure safe navigation when the capability of automatic risk response inadequate. This frame-work makes it easier to train data and the adjustment for machine learning based on Bayesian risk prediction. It can be concluded that the automation level can be increased and the workload of shore-based seafarers can be reduced easily.
topic Maritime Safety
Unmanned Ship
Unmanned Ship Navigation
On-board Decision-Making
Decision-Making Algorithm
Conversion Timing
Bayesian Risk Prediction
Seafarers
url http://www.transnav.eu/files/Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation,748.pdf
work_keys_str_mv AT ruolanzhang conversiontimingofseafarersdecisionmakingforunmannedshipnavigation
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