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

During pre-release, until the topic opens on Oct 28, 2026, you may contact the topic author directly with technical questions. After that, questions go through the Topic Q&A on DSIP.

Develop, integrate, and demonstrate an artificial intelligence (AI)-enabled computed vision and human-machine interface capability that transforms aerial refueling operations from subjective, observation-based evaluation into an objective, data-driven performance assessment and adaptive training environment. The system shall leverage advanced computer vision, machine learning, and aviation (flight dynamics) analytics techniques to automatically extract operationally relevant performance metrics from aerial refueling events, quantify deviations from desired flight profiles, and generate actionable post-flight feedback to improve aircrew proficiency, reduce human-factor-driven mishaps, and enhance mission readiness.The capability shall analyze video from cameras mounted on the tanker (esp. the KC-46) to provide near-real-time post-flight reconstruction of aerial refueling engagements by correlating aircraft position, motion dynamics, refueling geometry, pilot inputs, and operational context to identify performance trends and training opportunities. The developed system shall perform automated assessment of complex aerial refueling behaviors including aircraft stability, closure rate management, alignment accuracy, control surface position, other situational awareness indicators, and adherence to established refueling procedures.The objective is to mature existing advanced AI-enabled aviation analytics into an operationally relevant post-flight “hotwash” and training capability that increases the instructional value of each aerial refueling sortie, reduces reliance on subjective human assessment, accelerates pilot proficiency development, decreases equipment damage associated with refueling deviations, and establishes a scalable foundation for future intelligent aviation training systems across Department of the Air Force and joint aviation communities.The resulting prototype shall demonstrate the ability to ingest operational aerial refueling data, autonomously generate objective performance assessments, provide intuitive visualization and debrief products, and support transition into existing aviation training, evaluation, and modernization ecosystems. Importantly, the system will also include automated security management of the video so that implementation is seamless and complies with security classification guidance.

The full objective, description, Phase I, II and III requirements and evaluation criteria are in the official topic on DSIP.