Date of Award
Spring 2005
Document Type
Thesis - Open Access
Degree Name
Master of Science in Human Factors & Systems
Department
Human Factors and Systems
Committee Chair
Dahai Liu, Ph.D.
Committee Member
Beth Blickensderfer, Ph.D.
Committee Member
Dan Macchiarella, Ph.D.
Abstract
The military intends to increase the number of UAVs in service while at the same time reducing the number of operators (Dixon, Wickens & Chang; 2004). To meet this demand, many of the current UAV operator functions will need to be automated. How automation is applied to modern systems is not fixed. Levels of automation exist along a continuum from fully manual to fully automatic. Two proposed levels of automation for future UAV systems are Management by Consent (MBC), where the operator selects the task to be executed, and Management by Exception (MBE), where the computer selects the task to be executed are. The optimum operator-to-vehicle ratio for future UAV systems is not yet known. It is expected that the optimum operator-to-vehicle ratio will vary with the level of automation applied to the system. Future systems may require the use of adaptive automation to ensure maximum human-machine performance across varying operator-to-vehicle ratios. This study aims to help determine what levels of automation are most appropriate for different operator-to-vehicle ratios and how adaptive automation should be applied in future UAV systems.
Scholarly Commons Citation
Wasson, Ryan J., "The Effect of Level of Automation and Operator-to-Vehicle Ratio on Operator Workload and Performance in Future UAV Systems" (2005). Master's Theses - Daytona Beach. 210.
https://commons.erau.edu/db-theses/210
