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Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks

Author

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  • Xiaoyuan Zhao

    (School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China)

  • Haiwen Yuan

    (School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
    National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China)

  • Qing Yu

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China)

Abstract

The prototypes of autonomous vessels are expected to come into service within the coming years, but safety concerns remain due to complex traffic and natural conditions (e.g., Yangtze River). However, the response of autonomous vessels to potential accidents is still uncertain. The accident prevention for autonomous vessels is unconvincing due to the lack of objective studies on the causation analysis for maritime accidents. This paper constitutes an attempt to cover the aforementioned gap by studying the potential causations for maritime accidents in the Yangtze River by using a Bayesian-based network training approach. More than two hundred accidents reported between 2013 and 2019 in the Yangtze River are collected. As a result, a Bayesian network (BN) is successfully established to describe the causations among different risk influencing factors. By analysing the BN, this study reveals that the occurrence of maritime accidents (e.g., collision, grounding) can be expected to reduce with the development of autonomous vessels as the crews are removed. However, the extent of the consequences from some accidents (e.g., fire, critical weathers) could be more serious than conventional ones. Therefore, more attention and thoughts are needed to ensure the safe navigation of autonomous vessels in the Yangtze River.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9985-:d:630128
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    References listed on IDEAS

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    Cited by:

    1. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Li, Huanhuan & Çelik, Cihad & Bashir, Musa & Zou, Lu & Yang, Zaili, 2024. "Incorporation of a global perspective into data-driven analysis of maritime collision accident risk," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    3. Krzysztof Wróbel & Mateusz Gil & Yamin Huang & Ryszard Wawruch, 2022. "The Vagueness of COLREG versus Collision Avoidance Techniques—A Discussion on the Current State and Future Challenges Concerning the Operation of Autonomous Ships," Sustainability, MDPI, vol. 14(24), pages 1-20, December.

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