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dc.contributor.authorBrandsæter, Andreas
dc.contributor.authorOsen, Ottar
dc.date.accessioned2021-09-02T13:12:47Z
dc.date.available2021-09-02T13:12:47Z
dc.date.created2021-06-04T08:32:40Z
dc.date.issued2021
dc.identifier.issn1748-006X
dc.identifier.urihttps://hdl.handle.net/11250/2772619
dc.description.abstractThe advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.en_US
dc.language.isoengen_US
dc.publisherSAGEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleAssessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networksen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.journalProceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliabilityen_US
dc.identifier.doi10.1177/1748006X211021040
dc.identifier.cristin1913673
dc.description.localcode© 2020. This is the authors' accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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