Enhancing Small UAS Detection and Situational Awareness through Monte Carlo Visual Limit Modeling and Cognitive Tool Augmentation
Keywords:
Augmented reality, Cognitive augmentation, Monte Carlo simulation, Small UAS detection, Visual limitationsAbstract
The growing presence of small unmanned aircraft systems (sUAS) in airspace shared with manned aircraft has significantly increased the risk of mid-air collisions. Traditional visual-based detection by human pilots is often ineffective due to fundamental visual limitations, resulting in inadequate see-and-avoid performance. This research introduces an integrated approach combining Monte Carlo simulation of human visual detection limits with cognitive augmentation tools, specifically augmented reality (AR) heads-up displays. Through quantitative modeling, we compare baseline (unaided human vision) detection performance to scenarios enhanced by AR cues highlighting drone positions. Simulation results demonstrate a substantial improvement in detection probabilities, increasing from below 10% in unaided scenarios to approximately 85–90% when augmented. Moreover, the augmented system significantly extends detection range and warning time, providing pilots with crucial additional seconds to initiate safe avoidance maneuvers. Human factors implications such as pilot workload, trust in automation, and situational awareness are analyzed, emphasizing the need for carefully designed AR interfaces. These findings advocate strongly for adopting AR-based cognitive augmentation to enhance aviation safety, outlining clear recommendations for future development and operational deployment.
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