AI-Enabled Test & Evaluation Toolkit for Pilot Multi-Function Displays

RT&L Focus Area: Artificial Intelligence & Machine Learning - First introduced in 1972, the Fairchild A-10 Thunderbolt II has seen nearly five decades of success. To keep the platform mission ready, the 309th Software Engineering Group (309SWEG), designs, builds, tests, and deploys software upgrades to its multi-function display and pilot vehicle interface (MFD/PVI). The potential for error in their work and the harm it could inflict translates to lengthy test and evaluation (T&E) processes. Those processes add years to software rollout, stagnating both A-10 and more broadly Air Force modernization efforts while our adversaries march forward. Critically, an update to a single line of code for the MFD/PVI can take up-to 2 years to push into operations due to T&E alone. With the 309SWEG, CrowdAI seeks to reduce the time to complete visual inspection of MFD/PVI using computer vision, a form of artificial intelligence, to evaluate that interface during T&E. CrowdAI can address this critical challenge to aircraft modernization by leveraging artificial intelligence (AI). Using computer vision, a subfield of AI, CrowdAI software will evaluate the MFD/PVI, just as a pilot would see them, allowing 309SWEG System Test Engineers to reconcile the flight computer commands to the MFD/PVI with what is visually displayed. But, unlike a human, who tires under cognitive load and prolonged tasks, AI can process video longer and even faster than it can be collected. AI reduces the labor required for manual review of video, freeing personnel to higher-cognitive tasks. Long term, similar capabilities could be applied to other combat aircraft, not just the A-10, as well integrate at all different stages of testing, not just development, but acceptance, and operational.      Under Phase I award, CrowdAI will provide a product demonstration to the 309SWEG, Air Force Test Center (AFTC) and other stakeholders for a potential Phase II. CrowdAI will work with 309SWEG to identify customers and stakeholders key to transitioning the solution into the enterprise, presumably within Platform One. CrowdAI will evaluate anticipated project data for suitability with an AI solution that would process MFD/PVI data. To evaluate this data, Air Force Research Lab has agreed to host it in its Global Unified Environment, a CUI-accredited cloud compute environment, where CrowdAI software is already deployed.  CrowdAI machine learning models solve specific use-cases, exploiting virtually any form of imagery collected by ground, aerial, and satellite platforms—including full motion video and still imagery sensors. These models can evaluate greater quantities of imagery than an analyst and in a fraction of the time—and AI never tires, reducing the probability of error. For this SBIR Phase I, CrowdAI will work with 309SWEG to assess mission data and requirements in preparations for curating training sets and to train CV models in a Phase II.