The Impact of Increasing Autonomy on Training Requirements in a UAV Supervisory Control Task. (December 2019)

• Record Type: Journal Article
• Authors: Cummings, Mary; Huang, Lixiao; Zhu, Haibei; Finkelstein, Daniel; Wei, Ran
• Other Names: Baber Chris guest-editor.; Jones Patricia guest-editor.
• Is Part Of: Journal of cognitive engineering and decision making. Volume 13:Number 4(2019:Dec.)
• Page Start: 295
• Page End: 309
• Publication Date: 2019-12
• Subjects: automation -- autonomy -- complacency -- topics -- training -- trust -- unmanned; Human-computer interaction -- Periodicals; User-centered system design -- Periodicals; 004.019
• DOI: 10.1177/1555343419868917
• Languages: English
• ISSNs: 1555-3434

Abstract

A common assumption across many industries is that inserting advanced autonomy can often replace humans for low-level tasks, with cost reduction benefits. However, humans are often only partially replaced and moved into a supervisory capacity with reduced training. It is not clear how this shift from human to automation control and subsequent training reduction influences human performance, errors, and a tendency toward automation bias. To this end, a study was conducted to determine whether adding autonomy and skipping skill-based training could influence performance in a supervisory control task. In the human-in-the-loop experiment, operators performed unmanned aerial vehicle (UAV) search tasks with varying degrees of autonomy and training. At the lowest level of autonomy, operators searched images and, at the highest level, an automated target recognition algorithm presented its best estimate of a possible target, occasionally incorrectly. Results were mixed, with search time not affected by skill-based training. However, novices with skill-based training and automated target search misclassified more targets, suggesting a propensity toward automation bias. More experienced operators had significantly fewer misclassifications when the autonomy erred. A descriptive machine learning model in the form of a hidden Markov model also provided new insights for improved training protocols and interventional technologies.