AI Enabled Maneuver Identification via the Maneuver Identification Challenge
Kaira Samuel, Matthew LaRosa, Kyle McAlpin, Morgan Schaefer, Brandon, Swenson, Devin Wasilefsky, Yan Wu, Dan Zhao, Jeremy Kepner

TL;DR
This paper presents a new AI challenge using real-world Air Force flight simulator data to improve maneuver classification and assessment, fostering AI development for pilot training enhancement.
Contribution
It introduces the first public release of USAF flight training data and establishes baseline AI models for maneuver identification and quality assessment.
Findings
Successful separation of good vs bad simulator data
Effective categorization of flight maneuvers
Baseline AI models established for future research
Abstract
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…
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Taxonomy
TopicsAerospace and Aviation Technology · Target Tracking and Data Fusion in Sensor Networks · Inertial Sensor and Navigation
