The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual...

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Veröffentlicht in:IEEE transactions on human-machine systems 2018-10, Vol.48 (5), p.452-467
Hauptverfasser: Hooey, Becky L., Kaber, David B., Adams, Julie A., Fong, Terrence W., Gore, Brian F.
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container_issue 5
container_start_page 452
container_title IEEE transactions on human-machine systems
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creator Hooey, Becky L.
Kaber, David B.
Adams, Julie A.
Fong, Terrence W.
Gore, Brian F.
description This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement technique-the NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment, and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex human-machine systems.
doi_str_mv 10.1109/THMS.2017.2759758
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subjects Automation
Autonomy
cognitive models
Complex systems
Load modeling
Measurement techniques
Moderators
remotely operated vehicles (ROVs)
Taxonomy
unmanned aerial vehicles (UAVs)
unmanned ground vehicles (UGVs)
unmanned underwater vehicles (UUVs)
Unmanned vehicles
Workload
Workloads
title The Underpinnings of Workload in Unmanned Vehicle Systems
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