Supervisory control of multiple autonomous vehicles raises many issues concerning the balance of system autonomy with human interaction for optimal operator situation awareness and system performance. An unmanned vehicle simulation designed to manipulate the application of automation was used to evaluate participants’ performance on image analysis tasks under two automation control schemes: adaptable (level of automation directly manipulated by participant throughout trials) and adaptive (level of automation adapted as a function of participants’ performance on four types of tasks). The results showed that while adaptable automation increased workload, it also improved change detection, as well as operator confidence in task-related decision-making.
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