Abstract
Ongoing trends in society point towards the adoption of intelligent agents across safety critical industries. In the maritime domain, artificially intelligent agents may soon be capable of autonomously performing collision and grounding avoidance (CAGA); a task traditionally performed by humans. Consequently, the role of humans is anticipated to change from those performing collision avoidance to those supervising an agent performing collision avoidance. One of the key concerns with regards to human factors is avoiding the out-of-the-loop performance problem where humans lose situation awareness (SA) and become susceptible to misinterpreting the agent’s decisions and planned actions. Despite previous research addressing human factors in autonomous shipping and remote control, few studies have focused on how to support the humans’ mental processes in this new role. Therefore, this study performed a goal-directed task analysis addressing goals, decisions, and SA requirements for human-supervised collision avoidance. Data was obtained from in situ observations and interviews with nine navigators onboard passenger ferries, an appraisal of the collision regulations, and of relevant company documentation. The task analysis identified specific SA requirements to make agents, capable of collision and grounding avoidance, transparent to their users. The results further indicate a change towards increased cognitive activities required to verify agent performance. Therefore, providing insight into the agents’ internal reasoning and actions becomes a key consideration in supporting future supervisors. Given the trends towards the application of artificially intelligent agents capable of autonomous behaviour, this study anticipates that transparency becomes an essential prerequisite for safe and effective human-autonomy system oversight.
Published Version
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