A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation

Abstract

The autonomy of the ship is bound to bring new risk challenges to its berthing/unberthing operations process. Potential damages resulting from these risks can range from minor hull deformations to substantial losses of assets and environmental harm. To address these risks in the process of berthing/unberthing operations of MASSs or mitigate them to an acceptable level, this paper aims to propose a risk analysis framework combining dynamic and static Bayesian network models, and which can consider the temporal correlation of the values of risk factors, to help ships avoid the occurrence of operational accidents. In this framework, a set of probabilistic model is included to calculate the real-time risk value of autonomous berthing operation, so as to analyze and solve the time-dependent problem of risk change with time during berthing/unberthing of autonomous ships. In addition, the developed model can determine the key influencing factors and possible key situations during the berthing/unberthing of autonomous ships, which can support the risk prevention and mitigation measures in the process of autonomous ship berthing/unberthing. Finally, the applicability of the proposed risk analysis framework is analyzed through a virtual simulation test case of autonomous berthing/unberthing.