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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Wasson, Ryan J."],"institution":null,"degree_name":"Master of Science in Human Factors & Systems","degree_level":"Thesis - Open Access","degree_discipline":"Human Factors and Systems","degree_department":null,"school":null,"contributors":["Dahai Liu","Elizabeth L. Blickensderfer","Nickolas D. Macchiarella"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-04-01T08:00:00Z","date_published":"2005-04-01T08:00:00Z","updated_at":"2026-07-27T19:25:37Z","subjects":["automation","operator-to-vehicle","workload","performance","UAV","Ergonomics","Operations Research, Systems Engineering and Industrial Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/210","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dahai Liu","Elizabeth L. Blickensderfer","Nickolas D. Macchiarella"]},{"key":"dc:creator","label":"Author","values":["Wasson, Ryan J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Human Factors and Systems"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Human Factors & Systems"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["automation","operator-to-vehicle","workload","performance","UAV","Ergonomics","Operations Research, Systems Engineering and Industrial Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/210"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["The Effect of Level of Automation and Operator-to-Vehicle Ratio on Operator Workload and Performance in Future UAV Systems"]}]}],"canonical_facts":{"dc:contributor":["Dahai Liu","Elizabeth L. Blickensderfer","Nickolas D. Macchiarella"],"dc:creator":["Wasson, Ryan J."],"dc:description.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.
"],"dc:identifier":["https://commons.erau.edu/db-theses/210"],"dc:subject":["automation","operator-to-vehicle","workload","performance","UAV","Ergonomics","Operations Research, Systems Engineering and Industrial Engineering"],"dc:title":["The Effect of Level of Automation and Operator-to-Vehicle Ratio on Operator Workload and Performance in Future UAV Systems"],"thesis:degree_discipline":["Human Factors and Systems"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Human Factors & Systems"]},"updated_at":"2026-07-27T19:25:37Z"}