Vigilance and Automation Dependence in Operation of Multiple Unmanned Aerial Systems (UAS): A Simulation Study.

OBJECTIVE: This simulation study investigated factors influencing sustained performance and fatigue during operation of multiple Unmanned Aerial Systems (UAS). The study tested effects of time-on-task and automation reliability on accuracy in surveillance tasks and dependence on automation. It also investigated the role of trait and state individual difference factors. BACKGROUND: Warm's resource model of vigilance has been highly influential in human factors, but further tests of its applicability to complex, real-world tasks requiring sustained attention are necessary. Multi-UAS operation differs from standard vigilance paradigms in that the operator must switch attention between multiple subtasks, with support from automation. METHOD: 131 participants performed surveillance tasks requiring signal discrimination and symbol counting with a multi-UAS simulation configured to impose low cognitive demands, for 2 hr. Automation reliability was manipulated between-groups. Five Factor Model personality traits were measured prior to performance. Subjective states were assessed with the Dundee Stress State Questionnaire. RESULTS: Performance accuracy on the more demanding surveillance task showed a vigilance decrement, especially when automation reliability was low. Dependence on automation on this task declined over time. State but not trait factors predicted performance. High distress was associated with poorer performance in more demanding task conditions. CONCLUSIONS: Vigilance decrement may be an operational issue for multi-UAS surveillance missions. Warm's resource theory may require modification to incorporate changes in information processing and task strategy associated with multitasking in low-workload, fatiguing environments. APPLICATION: Interface design and operator evaluation for multi-UAS operations should address issues including motivation, stress, and sustaining attention to automation.

Overload and automation-dependence in a multi-UAS simulation: Task demand and individual difference factors.

Future unmanned aerial systems (UAS) operations will require control of multiple vehicles. Operators are vulnerable to cognitive overload, despite support from system automation. This study tested whether attentional resource theory predicts impacts of cognitive demands on performance measures, including automation-dependence and stress. It also investigated individual differences in response to demands. One-hundred and 1 university student participants performed a multi-UAS simulation mission incorporating 2 surveillance tasks. Cognitive demands and level of automation (LOA) of key tasks were manipulated between-subjects. Results were partially consistent with predictions. Higher task demands impaired performance and elevated distress and workload, as expected. Higher LOA produced greater dependence on automation, but failed to mitigate workload. It was expected that, as the automation was quite reliable, participants would attempt to conserve resources by depending more on automation under high demand. In fact, the opposite tendency was observed. Individuals high in conscientiousness were especially likely to override the automation under high demand, apparently taking charge personally. Neuroticism and distress were also associated with performance, but results did not fit a resource theory interpretation. Thus, understanding impacts of overload in the multi-UAS context requires understanding operator strategy as well as resource insufficiency. Findings have implications for system design, and operator selection and training. (PsycInfo Database Record (c) 2020 APA, all rights reserved).