Automated Functions: Their Potential for Impact Upon Maritime Sociotechnical Systems

Licentiate thesis, 2020

The shipping industry is evolving towards an unknown and unpredictable future. There is speculation that in the next two decades the maritime industry will witness changes far exceeding those experienced over the past 100 years. The rapid development of artificial intelligence (AI), big data, automation and their impacts upon fully autonomous ships have the potential to transform the maritime industry. While change is inevitable in the maritime domain, automated solutions do not guarantee navigational safety, efficiency or improved seaway traffic management. Such dramatic change also calls for a more systematic approach to designing, evaluating and adopting new solutions into a system. Although intended to support operator decision-making needs and reduce operator workload, the outcomes might create unforeseen changes throughout other aspects of the maritime sociotechnical system. In the maritime industry, the human is seldom put first in technology design which paradoxically introduces human-automation challenges related to technology acceptance, use, trust, reliance and risk. The co-existence and challenges of humans and automation, as it pertains to navigation and navigational assistance, is explored throughout this licentiate.
 
This thesis considers the Sea Traffic Management (STM) Validation Project  as the context to examine low-level automation functions intended to enhance operator (both Navigators and Vessel Traffic Service Operators) navigational safety and efficiency. The STM functions are designed to improve information sharing between ships and from ship to shore such as: route sharing, enhanced monitoring, and route crosschecking. The licentiate is built on two different data collection efforts during 2017-2018 within the STM Validation project. The functions were tested on two user groups: Bridge Officers and Vessel Traffic Service Operators. All testing was completed in high-fidelity bridge simulators using traffic scenarios developed by subject matter experts.

The aim of this licentiate is to study the impact of low levels of automation on operator behavior, and to explore the broader impact upon the maritime sociotechnical system. A mixed-method approach was selected to address these questions and included the following: observations, questionnaires, numerical assessment of ship behavior, and post-simulation debrief group sessions. To analyze and discuss the data, grounded theory, subject matter expert consultation, and descriptive statistics were used. The results point towards a disruption in current working practices for both ship and shore operators, and an uncertainty about the overall impact of low-level automation on operator behaviour. Using a sociotechnical systems approach, gaps have been identified related to new technology testing and implementation. These gaps relate to the overall preparedness of the shipping industry to manage the evolution towards smarter ships. The findings discussed in this licentiate aim to promote further discussions about a quickly evolving industry concerning automation integration in shipping and the potential impact on human performance in safety critical operations.