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Towards supervisory risk control of autonomous ships

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  • Utne, Ingrid Bouwer
  • Rokseth, Børge
  • Sørensen, Asgeir J.
  • Vinnem, Jan Erik

Abstract

The objective of this paper is to outline a framework for online risk modelling for autonomous ships. There is a clear trend towards increased autonomy and intelligence in ships because it enables new functionality, as well as safer and more cost-efficient operations. Nevertheless, emerging risks are involved, related to lack of knowledge and operational experience with the autonomous systems, the dependency on complex software-based control systems, as well as a limited ability to verify the safe performance of such systems. The framework presented in the paper is the first step towards supervisory risk control, i.e., developing control systems for autonomous systems with risk management capabilities to improve the decision-making and intelligence of such systems. The framework consists of two main phases, (i) hazard identification and analysis through the systems theoretic process analysis (STPA), and (ii) generating risk models represented by Bayesian Belief Networks (BBN) based on the outcomes of the STPA. The application in the paper is aimed at autonomous ships, but the results of the paper have a general relevance for both manned and unmanned systems with different levels of autonomy, complexity, and major hazard potential.

Suggested Citation

  • Utne, Ingrid Bouwer & Rokseth, Børge & Sørensen, Asgeir J. & Vinnem, Jan Erik, 2020. "Towards supervisory risk control of autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:reensy:v:196:y:2020:i:c:s0951832018308160
    DOI: 10.1016/j.ress.2019.106757
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    References listed on IDEAS

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    1. Jan-Erik Vinnem, 2014. "Offshore Risk Assessment vol 1," Springer Series in Reliability Engineering, Springer, edition 3, number 978-1-4471-5207-1, January.
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    Cited by:

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    7. Fonseca, Tiago & Lagdami, Khanssa & Schröder-Hinrichs, Jens-Uwe, 2021. "Assessing innovation in transport: An application of the Technology Adoption (TechAdo) model to Maritime Autonomous Surface Ships (MASS)," Transport Policy, Elsevier, vol. 114(C), pages 182-195.
    8. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Yu, Qing & Liu, Kezhong & Yang, Zhisen & Wang, Hongbo & Yang, Zaili, 2021. "Geometrical risk evaluation of the collisions between ships and offshore installations using rule-based Bayesian reasoning," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. Wenjun Zhang & Yingjun Zhang & Weiliang Qiao, 2022. "Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    11. Johansen, Thomas & Blindheim, Simon & Torben, Tobias Rye & Utne, Ingrid Bouwer & Johansen, Tor Arne & Sørensen, Asgeir J., 2023. "Development and testing of a risk-based control system for autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    12. Wang, Yang & Chen, Peng & Wu, Bing & Wan, Chengpeng & Yang, Zaili, 2022. "A trustable architecture over blockchain to facilitate maritime administration for MASS systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    13. Li, Xue & Oh, Poong & Zhou, Yusheng & Yuen, Kum Fai, 2023. "Operational risk identification of maritime surface autonomous ship: A network analysis approach," Transport Policy, Elsevier, vol. 130(C), pages 1-14.
    14. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Mazurek, J. & Lu, L. & Krata, P. & Montewka, J. & Krata, H. & Kujala, P., 2022. "An updated method identifying collision-prone locations for ships. A case study for oil tankers navigating in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    16. Xiaoyuan Zhao & Haiwen Yuan & Qing Yu, 2021. "Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    17. Zhang, Hengqi & Geng, Hua & Zeng, Huarong & Jiang, Li, 2023. "Dynamic risk evaluation and control of electrical personal accidents," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    18. Cheng, Tingting & Utne, Ingrid Bouwer & Wu, Bing & Wu, Qing, 2023. "A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    19. Wenjun Zhang & Xiangkun Meng & Xue Yang & Hongguang Lyu & Xiang-Yu Zhou & Qingwu Wang, 2022. "A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    20. Ismail Kurt & Murat Aymelek, 2022. "Operational and economic advantages of autonomous ships and their perceived impacts on port operations," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 302-326, June.
    21. de Vos, Jiri & Hekkenberg, Robert G. & Valdez Banda, Osiris A., 2021. "The Impact of Autonomous Ships on Safety at Sea – A Statistical Analysis," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    22. Gil, Mateusz, 2021. "A concept of critical safety area applicable for an obstacle-avoidance process for manned and autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    23. Chang, Chia-Hsun & Kontovas, Christos & Yu, Qing & Yang, Zaili, 2021. "Risk assessment of the operations of maritime autonomous surface ships," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    24. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad & Banda, Osiris Valdez & van Gelder, Pieter, 2022. "A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    25. Xue Li & Kum Fai Yuen, 2022. "Autonomous ships: A study of critical success factors," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 24(2), pages 228-254, June.

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