A Taxonomy of Level of Automation in Intelligent Operational Supervisory Task
Authors: Aobo Wang, Beiyuan Guo, Shuqi Xue, Ting Jiang, Haifeng Bao
Abstract: With the application of automation technology, Human operators rely on automation functions or intelligent agents to conduct complex cognitive tasks, but this also leads to a series of human factor risks such as out-of-the-loop and first failure problems. An appropriate level of automation (LOA) design will help to alleviate the above human factors risks and improve the performance of human-machine cooperation, but the traditional LOA taxonomy is difficult to directly guide the human-machine function allocation of operational supervisory tasks. To characterize current LOA design practices, a literature review was conducted to review the LOA taxonomy of supervision and control tasks in related fields. This research summarizes the taxonomy dimensions of LOA. The intelligent operational supervisory task requires the operators to maintain a high degree of interaction and cooperation with the automation system. Therefore, we must shift the focus of LOA design to cognitive interaction tasks and takeover tasks. This research analyzes the characteristics of different dimensions of LOA taxonomy in the literature, and summarizes the LOA granularity of system task, cognitive interaction task and takeover task. On this basis, from the perspective of human-machine interaction, the LOA taxonomy of intelligent operational supervisory tasks is proposed. This research provides an important theoretical basis for human-machine function allocation scheme and system LOA design, and has important theoretical and practical significance for improving the human-machine interaction efficiency.
Keywords: Metro operation control center, level of automation, taxonomy, human-machine interaction, human-machine function allocation
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