Investigating decision-making in human-machine teams for the operation of Maritime Autonomous Surface Ships

Abstract

The introduction of uncrewed Maritime Autonomous Surface Ships is fundamentally changing how ships are operated. Due to the remote nature of MASS, decision-making will become more challenging as operators will lack the physical cues they would have had onboard a vessel, so they will be more dependent on the technological systems. Additionally, the higher levels of automation in MASS are changing the role of the operator within the human-machine team to mainly a supervisory role, as they will be monitoring the automated systems and transitions of control. It is therefore important to explore how system design can support operators’ ability to monitor the automated systems effectively and maintain their situational awareness, so that they can make appropriate decisions during operations. This thesis investigates how decisions are made in human-machine teams, to explore how MASS operators’ decision-making at Remote Control Centres can be supported. Firstly, to identify the decision-making factors that need to be explored, a systematic literature review was conducted to develop a model of seven key factors involved in decision-making in human-machine teams. The seven decision-making factors were then used as the foundation of the research programme and a mixed methods approach was used to investigate each factor. The mixed methods approach included using the Perceptual Cycle Model framework, Systematic Human Error Prediction and Reduction Approach, the Risk Management Framework and other human factors methods to investigate the decision-making factors. The methods were employed to understand how decision-making can be supported through the system’s design. This resulted in design considerations and principles, and mitigation strategies that could be considered when creating future MASS system concept designs. From the findings of the application of these methods, a user-centred design framework has been created for industry, to facilitate the application of the methods in future design of novel systems to support HMT decision-making. The research has demonstrated how human factors methods can be applied prospectively to generate user-centred design concepts before the users or systems exist.

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Identifiers

ORCID for Kirsty Mary Lynch: orcid.org/0000-0003-4952-3964

ORCID for Katie Plant: orcid.org/0000-0002-4532-2818

ORCID for Mark Young: orcid.org/0009-0001-2594-453X

ORCID for Dominic Taunton: orcid.org/0000-0002-6865-089X

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