Trust and Cognitive Load in semi-automated UAV operation

Martin Lochner, Andreas Duenser, Shouvojit Sarker
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引用次数: 7

Abstract

Trust in automation is an essential precursor to system adoption and use. Given the emerging wave of autonomous systems available for public consumption and the resources devoted to this trend, it's important to understand trust, and how to measure it. Further, the level of performance demonstrated by a system can affect trust in that system. As such, proper design of an autonomous system can be facilitated by measuring trust in such systems. Rather than relying only on traditional methods of measuring trust, such as pen and paper, or behavioural markers, this work extends previous research by investigating psycho-physiological markers for trust, using Galvanic Skin Response (GSR) and machine learning. We induced high vs. low trust states in amateur unmanned aerial vehicle (UAV) operators, and manipulated the automation level of the UAV. We collected workload and trust ratings during and after flying a UAV. Despite moderate results with traditional metrics (NASA TLX, and the System Trust Scale), we were able to classify trust states based on the GSR data with 80% accuracy. This research forms part of our ongoing work on developing a model for the relation between automation, and user trust and cognitive load.
无人机半自动化操作中的信任与认知负荷
对自动化的信任是系统采用和使用的基本前提。考虑到可供公众消费的自主系统的兴起以及为这一趋势投入的资源,理解信任以及如何衡量信任是很重要的。此外,系统所展示的性能水平会影响对该系统的信任。因此,通过衡量对这些系统的信任,可以促进自治系统的适当设计。这项工作不是仅仅依靠传统的测量信任的方法,比如笔和纸,或者行为标记,而是通过使用皮肤电反应(GSR)和机器学习来调查信任的心理生理标记,从而扩展了以前的研究。通过诱导业余无人机操作者的高信任状态和低信任状态,对无人机的自动化水平进行操纵。我们收集了无人机飞行期间和之后的工作量和信任评级。尽管传统指标(NASA TLX和系统信任量表)的结果一般,但我们能够基于GSR数据以80%的准确率对信任状态进行分类。这项研究构成了我们正在进行的开发自动化、用户信任和认知负荷之间关系模型的一部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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