A Quantitative Measure for Degree of Automation and Its Relation to System Performance and Mental Load

摘要

In this paper we quantitatively model degree of automation (DofA) in superviso- ry control as a function of the number and nature of tasks to be performed by the operator and automation. This model uses a task weighting scheme in which weighting factors are obtained from task demand load, task mental load, and task effect on system performance. The computation of DofA is demonstrated using an experimental system. Based on controlled experiments using operators, analyses of the task effect on system performance, the prediction and assessment of task demand load, and the prediction of mental load were performed.