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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Gdynia Maritime University
2017-09-01
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Online Access: | http://www.transnav.eu/files/Conversion Timing of Seafarer’s Decision-making for Unmanned Ship Navigation,748.pdf |
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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 AT masaofurusho conversiontimingofseafarersdecisionmakingforunmannedshipnavigation |
_version_ |
1725072818509447168 |