Safety assessment of MASS navigational performance in coastal voyage using cognitive reliability and Bayesian best worst approach

Maritime autonomous surface ships (MASS) represent new operational perspective which transfer navigational responsibilities from ship crew/operator to complex automated systems, specifically in coastal sea voyage where marine traffic congestion and environmental conditions pose significant risks. This paper performs a conceptual safety performance assessment of MASS navigation in coastal voyage systematically under cognitive human error prediction and Bayesian best–worst method (BWM). In the paper, while cognitive human error is predicted to quantify human–machine interaction (HMI) vulnerabilities, including perception, decision-making, and supervisory control tasks; the BWM can predict probabilistic priority weights for critical safety key tasks under uncertainty and expert judgment inconsistency. The outcome of the paper is showing that operational key task MFD₂ and CC₂ has the highest failure probability values affecting safety performance of MASS navigation in coastal voyage. Besides improving safety performance of autonomous ship navigation, the paper will contribute by providing a conceptual framework for determining critical navigational vulnerabilities, prioritizing safety factors, and supervisory strategies for designers, safety inspectors, ship owners, remote control centre operators and safety researchers.