Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Abstract

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g. difficulties monitoring automated systems) for human operators, with their ‘remoteness’ in shore-side control centres exacerbating issues. This paper aims to investigate underlying decision-making processes of operators of uncrewed vehicles using the theoretical foundation of the Perceptual Cycle Model (PCM). A case study of an Unmanned Aerial Vehicle (UAV) accident has been chosen as it bears similarities to the operation of MASS through means of a ground-based control centre. Two PCMs were developed; one to demonstrate what actually happened and one to demonstrate what should have happened. Comparing the models demonstrates the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when operating from remote control centres. Practitioner Summary: To investigate underlying decision-making processes of operators of uncrewed vehicles using the Perceptual Cycle Model, by using an UAV accident case study. The findings showed the importance of operator situational awareness, clearly defined operator roles, training and interface design in making decisions when monitoring uncrewed systems from remote control centres.