On the Development of Quantitative Operator Situational Awareness Assessment Methods for Small-Scale Unmanned Aircraft Systems

Small-Scale Unmanned Autonomous Systems
$(sUASs)$
have become an integral part of situation assessment and decision support tasks across a multitude of application domains. Evaluation methods that provide decisive comparisons between different sUAS platforms are critical for not only selecting suitable sUASs, but also for making sure that the chosen sUAS platform can satisfy the required minimum capabilities for a given application. Toward developing sUAS test methods, a new quantitative Operator Situation Awareness (OSA) assessment method is presented for evaluating and comparing sUAS platforms for their ability to provide adequate levels of Situation Awareness (SA) in subterranean (SubT) environments. The work presented here improves upon our previous work based on Attention Allocation Model
$(AAM)$
and Man-Machine Integration Design and Analysis (MIDAS)-based SA model by (a) applying formulas to the Salience, Effort, Expectancy, and Value (SEEV) model for accurate quantification and (b) utilizing improved experiments with new operationally relevant scenarios designed to validate those formulas. In particular, our new OSA assessment method accounts for spatial perception differences across platforms by introducing a new component that we refer to as Virtual Proportion, which is obtained from Attention Allocation Proportion of other other Situation Elements by comparing the correct rate of Situation Awareness Global Assessment Technique (SAGAT). Our approach is illustrated via USA comparison of two (02) military-grade sUAS platforms. The paper concludes with a discussion on potential future expansions.