Prediction of human–machine interface (HMI) operational errors for maritime autonomous surface ships (MASS)

The human factor is a hot topic for the maritime industry since more than 80 percent of maritime accidents are due to human error. Minimizing human error contributions in maritime transportation is vital to enhance safety levels. At this point, the maritime autonomous surface ships (MASS) concept has become one of the most significant aspects to minimize human errors. The objective of this research is to predict the human–machine interface (HMI)-based operational errors in autonomous ships to improve safety control levels. At this point, the interaction between shore-based operator and controlling system (cockpits) can be monitored and potential HMI operational errors can be predicted. This research utilizes a Success Likelihood Index Method (SLIM) under an interval type-2 fuzzy sets (IT2FSs) approach. While the SLIM provides a prediction of the human–machine interface (HMI) operational errors, the IT2FSs tackles uncertainty and vagueness in the decision-making process. The findings of this paper are expected to highlight the importance of human–machine interface (HMI) operational errors in autonomous ships not only for designers but also for operational aspects.