Multi-Unmanned Vehicle Supervisory Control: An Initial Evaluation of Personality Drivers

Abstract

Achieving an optimal balance of system autonomy with human interaction is critical for envisioned applications of multi-unmanned vehicle supervisory control. While research to date has helped inform control station interface design in terms of what to automate, when to automate, and how much to automate, more research is needed examining automation levels across tasks and to better understand what factors (personality, arousal, alerting, motivation, etc.) mediate effective automation reliance and automation transference (automation level of one task impacting performance of other tasks). The present experiment employed a multi-unmanned aerial vehicle simulation to support the collection of objective performance data on several mission-related tasks, as well as measures of individual differences (e.g., personality). Additionally, the autonomy level for two primary supervisory control tasks was manipulated. The results of this initial evaluation of personality drivers of supervisory control performance showed that individual difference data can vary as a function of automation configuration and that there is a complex interplay between personality factors and task type that may inform interface design.