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Measures of Attention in Autonomous and Semi-Autonomous Multi-Vehicle Supervision
Conference proceeding   Open access

Measures of Attention in Autonomous and Semi-Autonomous Multi-Vehicle Supervision

Ellen J Bass, Julie A Adams and Christopher A Sanchez
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
01 Jan 2022
DOI: https://doi.org/10.1109/ICHMS56717.2022.9980754
url
https://doi.org/10.1136/jech-2022-219335View
Published, Version of Record (VoR)CC BY V4.0,  Open

Abstract

Aptitudes Autonomy Color vision Eye movements Human factors Man machine systems Man-machine interfaces Multiple target tracking Robotics
Conference Title: 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS) Conference Start Date: 2022, Nov. 17 Conference End Date: 2022, Nov. 19 Conference Location: Orlando, FL, USAAttention is critical for a single supervisor responsible for multiple autonomous or semi-autonomous vehicles due to the vehicle monitoring demands from using a control station or other user interface. We conducted a review of the human factors and robotics literature to identify measures of attention and attention-related aptitudes in human-autonomy teams (HAT) studies. In sixteen published studies, we identified 17 measures of attentional allocation and control; most were direct or derived measures of visual gaze obtained by eye tracking. Five measures of attention-related aptitudes included perceived attentional control, color vision, multiple object tracking, visual search, and working memory capacity. We discuss the measures regarding their respective psychological constructs and how they relate to other human factors. The reported measures represent the state of the art in the field of HAT and may be used to support further investigation and analyses, such as the effectiveness of training programs and human-machine interfaces.

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