2026-09-19T16:21:21Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/671902022-01-13T07:54:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Mary L. Cummings.
Mkrtchyan, Armen A
Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2011-11-18T20:58:15Z
2011-11-18T20:58:15Z
2011
2011
http://hdl.handle.net/1721.1/67190
758653854
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 151-156).
Currently, numerous automated systems need constant monitoring but require little to no operator interaction for prolonged periods, such as unmanned aerial systems, nuclear power plants, and air traffic management systems. This combination can potentially lower operators' workload to dangerously low levels, causing boredom, lack of vigilance, fatigue, and performance decrements. As more systems are automated and placed under human supervision, this problem will become more prevalent in the future. To mitigate the problem through predicting operator performance in low task load supervisory domains, a queuing-based discrete event simulation model has been developed. To test the validity and robustness of this model, a testbed for single operator decentralized control of unmanned vehicles was utilized, simulating a low workload human supervisory control (HSC) environment. Using this testbed, operators engaged in a four-hour mission to search, track, and destroy simulated targets. Also, a design intervention in the form of cyclical auditory alerts was implemented to help operators sustain directed attention during low task load environments. The results indicate that the model is able to accurately predict operators' workload. Also, the model predicts operators' performance reasonably well. However, the inability of the model to account for operator error is a limiting factor that lowers model's accuracy. The results also show that the design intervention is not useful for operators who do not have difficulties sustaining attention for prolonged periods. The participants of this study were exceptional performers, since most of them had very high performance scores. Further research will investigate the possibility of conducting another low task load, long duration study with a more diverse set of participants to assess the impact of the design intervention and to extract personality traits that may affect system performance. Also, the model needs to be revised to take into account operator errors, which can significantly affect performance of HSC systems.
by Armen A. Mkrtchyan.
S.M.
156 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Aeronautics and Astronautics.
Modeling operator performance in low task load supervisory domains
Modeling cyclical attention switching strategies in low workload supervisory domains
Thesis
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Publication
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<Abstract>Currently, numerous automated systems need constant monitoring but require little to no operator interaction for prolonged periods, such as unmanned aerial systems, nuclear power plants, and air traffic management systems. This combination can potentially lower operators' workload to dangerously low levels, causing boredom, lack of vigilance, fatigue, and performance decrements. As more systems are automated and placed under human supervision, this problem will become more prevalent in the future. To mitigate the problem through predicting operator performance in low task load supervisory domains, a queuing-based discrete event simulation model has been developed. To test the validity and robustness of this model, a testbed for single operator decentralized control of unmanned vehicles was utilized, simulating a low workload human supervisory control (HSC) environment. Using this testbed, operators engaged in a four-hour mission to search, track, and destroy simulated targets. Also, a design intervention in the form of cyclical auditory alerts was implemented to help operators sustain directed attention during low task load environments. The results indicate that the model is able to accurately predict operators' workload. Also, the model predicts operators' performance reasonably well. However, the inability of the model to account for operator error is a limiting factor that lowers model's accuracy. The results also show that the design intervention is not useful for operators who do not have difficulties sustaining attention for prolonged periods. The participants of this study were exceptional performers, since most of them had very high performance scores. Further research will investigate the possibility of conducting another low task load, long duration study with a more diverse set of participants to assess the impact of the design intervention and to extract personality traits that may affect system performance. Also, the model needs to be revised to take into account operator errors, which can significantly affect performance of HSC systems.</Abstract>
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