Title:
Virtual Workload Measurement for Assessing Systems Utilizing Automation Technology

Thumbnail Image
Files
scitech2021_guckenberger_workload.pdf (2.83 MB)
Author(s)
Guckenberger, Matthew
 Sudol, Alicia M.
 Mavris, Dimitri N.
Authors
Advisor(s)
Advisor(s)
Editor(s)
Associated Organization(s)
Organizational Unit
Daniel Guggenheim School of Aerospace Engineering
The Daniel Guggenheim School of Aeronautics was established in 1931, with a name change in 1962 to the School of Aerospace Engineering
Organizational Unit
Organizational Unit
Series
Collections
Research Publications
Supplementary to
Permanent Link
http://hdl.handle.net/1853/63988
Abstract
With the increasing integration of automation technologies, the role of the operator is changing from sole actor to a shared supervisor/actor role. Studies on unmanned ground vehicle operators and recent crashes partially blamed on automation technologies demonstrate the need to measure and assess operator awareness and workload. Overcoming these challenges requires an assessment early in the design cycle for operator awareness and workload. This methodology integrates concepts from cognitive engineering into operations analysis to better capture and analyze the effectiveness of increasingly automated systems. An agent-based model is created using Operational Event Sequence Diagrams and concepts from situation awareness research to guide agent formulation. The agent rule set is then mapped to the NASA Task Load Index scales to provide a dynamic output throughout the simulation. A traffic model is built in AFSIM to compare the mental workload associated with city versus highway driving. The dynamic workload measurement is the first step in a framework which will enable automation technologies to be traded during the conceptual design phase.
Sponsor
Date Issued
2021-01
Extent
Resource Type
Text
Resource Subtype
Paper
Rights Statement
Rights URI