From Desk to Field - Human Factor Issues in Remote Monitoring and Controlling of Autonomous Unmanned Vessels

Man, Yemao; Lundh, Monica; Porathe, Thomas; MacKinnon, Scott

doi:10.1016/j.promfg.2015.07.635

Cited by 67 publications

(53 citation statements)

References 10 publications

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“…Technical failures can play an important role at the outset, as in most newly systems; as experience increases, they can be expected to be overcome. But human error would continue to be a huge challenge for unmanned ships [67][68][69]. e shore-based control of the ship contains new safety issues and an interesting question will be the interaction of manned and unmanned vessels in the same tra c zone.…”

Section: Probability Distribution Of Fuzzy Bayesian Networkmentioning

confidence: 99%

“…e spatial movement of ships (e.g., rolling, slamming, pitching, or heaving) and the inertial performance of ships with relation to maneuverability are all contribute to the decisions made regarding vessel handling [68]. However, when the unmanned ship concept is introduced, it will be a big di culty to maintain a high-level of situation awareness [67].…”

Section: Influencing Factors Of Navigation Safety Risk Of Unmanned Namentioning

confidence: 99%

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Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Zhang

Yang

et al. 2019

Journal of Advanced Transportation

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Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.

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Section: Probability Distribution Of Fuzzy Bayesian Networkmentioning

confidence: 99%

Section: Influencing Factors Of Navigation Safety Risk Of Unmanned Namentioning

confidence: 99%

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Zhang

Yang

et al. 2019

Journal of Advanced Transportation

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“…Following this, a safety control structure was elaborated. This was achieved by reviewing the available literature pertaining to unmanned merchant vessels, including [9,11,15,16,[22][23][24][25][26].…”

Section: System-theoretic Process Analysis -Stpamentioning

confidence: 99%

A method for uncertainty assessment and communication in safety-driven design - a case study of unmanned merchant vessel

Krzysztof¹,

Jakub²

2018

IJSS

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“…The maritime domain, which is one the most traditional and conservative of industries, is currently in the transition phase driven by this fastchanging technology landscape. Many digitalized products, applications and services have been introduced into the shipping industry to provide maritime users a plethora of platforms and decision support tools to access information and maintain system control [7], such as energy-efficient fuel monitoring systems [8], unmanned cargo handling systems in the world's largest automated terminal [9], autonomous unmanned ships [10][11][12], etc. Intelligent systems are constantly shifting the human operators' role.…”

Section: Introductionmentioning

confidence: 99%

Facing the New Technology Landscape in the Maritime Domain: Knowledge Mobilisation, Networks and Management in Human-Machine Collaboration

Man

Lundh

MacKinnon

2018

Advances in Intelligent Systems and Computing

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Rapid development in artificial intelligence and big data analytic applications have had a significant impact on knowledge mobilisation across industries including the shipping domain. This paper uses the practice of energy efficiency onboard ships as a case study to discuss how knowledge mobilisation should address this context change and uncovers how existing knowledge networks in the shipping industry would evolve in this emerging data-driven ecology. From a systems perspective, it suggests hallmarks associated with the knowledge mobilisation processes in the new technology landscape. This paper shapes a discussion intended to derive design and management implications of the system infrastructure contributing to a safe, efficient and sustainable shipping business model and provide insights on knowledge adaption in the emerging human-machine collaboration context.

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From Desk to Field - Human Factor Issues in Remote Monitoring and Controlling of Autonomous Unmanned Vessels

Cited by 67 publications

References 10 publications

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

Safety Risk Analysis of Unmanned Ships in Inland Rivers Based on a Fuzzy Bayesian Network

A method for uncertainty assessment and communication in safety-driven design - a case study of unmanned merchant vessel

Facing the New Technology Landscape in the Maritime Domain: Knowledge Mobilisation, Networks and Management in Human-Machine Collaboration

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