# Maneuver Identification Challenge

arXiv: https://arxiv.org/abs/2108.11503 · HTML: https://mlsanity.com/paper/2108.11503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [Mathematical Foundations of the GraphBLAS](https://mlsanity.com/paper/1606.05790/) (2016)
- [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)
- [Signal Enhancement for Magnetic Navigation Challenge Problem](https://mlsanity.com/paper/2007.12158/) (2020)
- [Static Graph Challenge: Subgraph Isomorphism](https://mlsanity.com/paper/1708.06866/) (2017)
- [Big Data Dimensional Analysis](https://mlsanity.com/paper/1408.0517/) (2014)
- [Enabling On-Demand Database Computing with MIT SuperCloud Database Management System](https://mlsanity.com/paper/1506.08506/) (2015)
- [D4M: Bringing Associative Arrays to Database Engines](https://mlsanity.com/paper/1508.07371/) (2015)
- [Optimizing Xeon Phi for Interactive Data Analysis](https://mlsanity.com/paper/1907.03195/) (2019)

## Abstract (arXiv)

AI algorithms that identify maneuvers from trajectory data could play an important role in improving flight safety and pilot training. AI challenges allow diverse teams to work together to solve hard problems and are an effective tool for developing AI solutions. AI challenges are also a key driver of AI computational requirements. The Maneuver Identification Challenge hosted at maneuver-id.mit.edu provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions of maneuvers, and examples of these maneuvers performed by experienced pilots. Each trajectory consists of positions, velocities, and aircraft orientations normalized to a common coordinate system. Construction of the data set required significant data architecture to transform flight simulator logs into AI ready data, which included using a supercomputer for deduplication and data conditioning. There are three proposed challenges. The first challenge is separating physically plausible (good) trajectories from unfeasible (bad) trajectories. Human labeled good and bad trajectories are provided to aid in this task. Subsequent challenges are to label trajectories with their intended maneuvers and to assess the quality of those maneuvers.