  Artificial intelligence (AI) has enormous potential to improve Air Force
pilot training by providing actionable feedback to pilot trainees on the
quality of their maneuvers and enabling instructor-less flying familiarization
for early-stage trainees in low-cost simulators. Historically, AI challenges
consisting of data, problem descriptions, and example code have been critical
to fueling AI breakthroughs. The Department of the Air Force-Massachusetts
Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such
an AI challenge using real-world Air Force flight simulator data. The Maneuver
ID challenge assembled thousands of virtual reality simulator flight recordings
collected by actual Air Force student pilots at Pilot Training Next (PTN). This
dataset has been publicly released at Maneuver-ID.mit.edu and represents the
first of its kind public release of USAF flight training data. Using this
dataset, we have applied a variety of AI methods to separate "good" vs "bad"
simulator data and categorize and characterize maneuvers. These data,
algorithms, and software are being released as baselines of model performance
for others to build upon to enable the AI ecosystem for flight simulator
training.
