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人为-自动化团队中的操作员选择:手动任务技能在预测自动化故障干预中的作用。

Operator selection for human-automation teaming: The role of manual task skill in predicting automation failure intervention.

机构信息

The University of Western Australia, Australia.

The University of Western Australia, Australia.

出版信息

Appl Ergon. 2024 Jul;118:104288. doi: 10.1016/j.apergo.2024.104288. Epub 2024 Apr 17.

Abstract

Humans working in modern work systems are increasingly required to supervise task automation. We examined whether manual aircraft conflict detection skill predicted participants' ability to respond to conflict detection automation failures in simulated air traffic control. In a conflict discrimination task (to assess manual skill), participants determined whether pairs of aircraft were in conflict or not by judging their relative-arrival time at common intersection points. Then in a simulated air traffic control task, participants supervised automation which either partially or fully detected and resolved conflicts on their behalf. Automation supervision required participants to detect when automation may have failed and effectively intervene. When automation failed, participants who had better manual conflict detection skill were faster and more accurate to intervene. However, a substantial proportion of variance in failure intervention was not explained by manual conflict detection skill, potentially reflecting that future research should consider other cognitive skills underlying automation supervision.

摘要

在现代工作系统中工作的人类越来越需要监督任务自动化。我们研究了手动飞机冲突检测技能是否可以预测参与者在模拟空中交通管制中对冲突检测自动化故障做出反应的能力。在冲突辨别任务(评估手动技能)中,参与者通过判断飞机相对到达公共交叉点的时间来判断飞机是否存在冲突。然后,在模拟空中交通管制任务中,参与者监督自动化系统代表他们部分或完全检测和解决冲突。自动化监督要求参与者检测自动化系统何时可能出现故障,并进行有效的干预。当自动化系统出现故障时,手动冲突检测技能更好的参与者可以更快、更准确地进行干预。然而,故障干预的很大一部分差异无法用手动冲突检测技能来解释,这可能表明未来的研究应该考虑自动化监督的其他认知技能。

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