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Predictive Model for Workload in Remote Operators During sUAS Contingency Scenarios  (2023)
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The increase in automated capabilities of small Uncrewed Aerial Systems (sUAS) has enabled the human operators to manage larger numbers of vehicles simultaneously. As this happens, the operational paradigm shifts to an m:N configuration where multiple operators (𝑚) are managing multiple vehicles (𝑁) together. However, many questions about how operators will interact with each other and share interaction across the vehicle pool are yet unanswered. Therefore, stakeholders from government and industry have partnered to develop ground control station concepts for such operations. The work presented in this paper aims to identify factors that contribute to operator workload. A supervised machine learning-based method built using Support Vector Machines and K-fold cross-validation was used to create workload prediction models for various NASA TLX subscales by leveraging features related to interactions and their relative timings during m:N operations. Results show that the models yielded fairly high predictive accuracies ranging from ∼60-75%.
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Aerial, automation, Operators, Remote, small, sUAS, Systems, Uncrewed, Workload
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In AIAA AVIATION 2023 Forum (p. 3334)
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Adobe PDF Icon  AIAA_Aviation2023_SVM_Model.pdf (Download Acrobat Reader Click to download Adobe Acrabat Reader)
  (672KB) (application/pdf)
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Curator: Phil So
NASA Official: Jessica Nowinski
Last Updated: August 15, 2019