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12 May 2016 Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment
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Future unmanned vehicle operations will see more responsibilities distributed among fewer pilots. Current systems typically involve a small team of operators maintaining control over a single aerial platform, but this arrangement results in a suboptimal configuration of operator resources to system demands. Rather than devoting the full-time attention of several operators to a single UAV, the goal should be to distribute the attention of several operators across several UAVs as needed. Under a distributed-responsibility system, operator task load would be continuously monitored, with new tasks assigned based on system needs and operator capabilities. The current paper sought to identify a set of metrics that could be used to assess workload unobtrusively and in near real-time to inform a dynamic tasking algorithm. To this end, we put 20 participants through a variable-difficulty multiple UAV management simulation. We identified a subset of candidate metrics from a larger pool of pupillary and behavioral measures. We then used these metrics as features in a machine learning algorithm to predict workload condition every 60 seconds. This procedure produced an overall classification accuracy of 78%. An automated tasker sensitive to fluctuations in operator workload could be used to efficiently delegate tasks for teams of UAV operators.
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Samuel S. Monfort, Ciara M. Sibley, and Joseph T. Coyne "Using machine learning and real-time workload assessment in a high-fidelity UAV simulation environment", Proc. SPIE 9851, Next-Generation Analyst IV, 98510B (12 May 2016);

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