AI-Enabled Computed Vision Air-to-Air Refueling Performance Assessment and Automated Pilot Debrief System

STTR topic DAF27TZ01-DV001 (U.S. Department of Defense – Air Force), Phase II. Open to U.S. small businesses; STTR projects partner with a U.S. research institution. Opens: October 28, 2026. Closes: November 25, 2026. Air-to-air refueling (AAR) is a critical Department of the Air Force and Department of the Navy capability enabling global mobility, long-range strike, combat airpower projection, and sustained aviation operations. However, aerial refueling remains one of the most technically demanding and safety-critical aviation tasks performed by aircrews. Successful execution requires precise aircraft positioning, coordinated control inputs, accurate closure rate management, continuous situational awareness, and effective crew coordination between tanker and receiver aircraft.Current aerial refueling training and post-flight assessment processes rely heavily on instructor observation, pilot self-assessment, manual video review, and subjective evaluation of performance. These approaches provide limited ability to quantitatively assess the thousands of individual performance variables that influence successful refueling operations. Existing methods do not provide pilots with objective, moment-by-moment feedback on aircraft positioning, closure rates, control inputs, deviations from desired procedures, or trends in individual performance over time.This topic seeks to develop, demonstrate, and transition an artificial intelligence (AI)-enabled computed vision capability that (post-flight) automatically analyzes aerial refueling operations and provides objective, data-driven pilot performance feedback. The desired capability will mature existing advances in autonomous aerial refueling analytics and apply them toward training, readiness, and safety improvement by transforming aerial refueling video and available aircraft performance data into actionable post-flight debrief products.The developed system shall provide an automated “hot wash” which analyzes aerial…