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EN
Automation transparency is a means to provide understandability and predictability of autonomous systems by disclosing what the system is currently doing, why it is doing it, and what it will do next. To support human supervision of autonomous collision avoidance systems, insight into the system’s internal reasoning is an important prerequisite. However, there is limited knowledge regarding transparency in this domain and its relationship to human supervisory performance. Therefore, this paper aims to investigate how an information processing model and a cognitive task analysis could be used to drive the development of transparency concepts. Also, realistic traffic situations, reflecting the variation in collision type and context that can occur in real-life, were developed to empirically evaluate these concepts. Together, these activities provide the groundwork for exploring the relation between transparency and human performance variables in the autonomous maritime context.
EN
At NTNU in Norway an 8-year research project has been established to (among other things) research the interaction between humans and unmanned, autonomous ships. The human will become even more important when ship operator will be located remotely in shore control centers ashore. This concept paper will take a closer look on remote decision-making by operators monitoring several ships. How can interface design help them to get quickly into-the-loop when something unexpected suddenly happens? I will in this paper suggest keeping a copy of the AI expert-system controlling the ship, updated and running in parallel in the control center to keep the operator’s situation awareness during short communication glitches. Also, to design a “quickly-getting-into-the-loop-display” which automatically will appear in an alarm situation, allowing the operator just-in-time and simple-to-understand information. I will also stress the important of the concept automation transparency.
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