# AI Enabled Maneuver Identification via the Maneuver Identification Challenge

arXiv: https://arxiv.org/abs/2211.15552 · HTML: https://mlsanity.com/paper/2211.15552/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Sparse Deep Neural Network Graph Challenge](https://mlsanity.com/paper/1909.05631/) (2019)
- [Streaming Graph Challenge: Stochastic Block Partition](https://mlsanity.com/paper/1708.07883/) (2017)
- [Static Graph Challenge: Subgraph Isomorphism](https://mlsanity.com/paper/1708.06866/) (2017)

## Abstract (arXiv)

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.