Conversion timing of seafarer’s decision-making for unmanned ship navigation

• Autorzy: Zhang R. L. , Furusho M.

Identyfikatory

DOI

10.12716/1001.11.03.11

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

maritime safety; unmanned ship; unmanned ship navigation; on-board decision-making; decision-making algorithm; conversion timing; bayesian risk prediction; seafarers

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2017
• Tom: Vol. 11, no. 3
• Strony: 463--468
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Zhang R. L.

• Kobe University, Kobe, Japan

autor

Furusho M.

• Kobe University, Kobe, Japan

Bibliografia

• 1 Endsley, M. R. & Kiris, E. O. 1995. The out‐of‐the‐loop performance problem and level of control in automation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(2): 381‐394.
• 2 Parasuraman, R., Sheridan T. B. & Wickens C. D. 2000. A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics‐Part A: Systems and Humans, 30(3): 286‐297.
• 3 Prashanth, C. R., Sagar T., Bhat N. 2013. Obstacle detection & elimination of shadows for an image processing based automated vehicle. Advances in Computing, Communications and Informatics (ICACCI) International Conference on. IEEE, 2013: 367‐372.
• 4 Sarda, E. I., Qu, H., Bertaska, I. R. 2016. Station‐keeping control of an unmanned surface vehicle exposed to current and wind disturbances. Ocean Engineering, 127: 305‐324.
• 5 Hocraffer A., & Nam C. S. 2017. A meta‐analysis of humansystem interfaces in unmanned aerial vehicle (UAV) swarm management. Applied Ergonomics, 58: 66‐80.
• 6 Ren W, Beard R W, Atkins E M. 2007. Information consensus in multivehicle cooperative control. IEEE Control Systems Magazine, 129(2):571‐583.
• 7 Gudelj A., & Krčum M. 2012. The Container Transportation Problem: Model and Solution Methods. First International Conference on Traffic and Transport Engineering.
• 8 Yang, C., & Wang, N. 2011. Decision‐making Method for Berth Allocation Disruption Management in Container Terminal. Operations Research and Management Science, 4: 14.
• 9 Dubrovsky, V. A. 2010. Multi‐hulls: new options and scientific developments. Ships and Offshore Structures, 5(1): 81‐92.
• 10 Kirsch, A. 2016. Human‐aware Navigation in Domestic Environments Using Heuristic Decision‐Making.
• 11 Metzger, U. & Parasuraman R. 2005. Automation in future air traffic management: Effects of decision aid reliability on controller performance and mental workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 47(1): 35‐49.
• 12 Zhang, R. & Furusho, M. 2016. Constructing a Decision‐ Support System for Safe Ship‐Navigation Using a Bayesian Network. International Conference on Human‐ Computer Interaction. Springer International Publishing, 616‐628.
• 13 Goodfellow, I., Pouget‐Abadie, J., Mirza, M. 2014. Generative adversarial nets. Advances in Neural Information Processing Systems 2672‐2680.