Maneuver Identification Challenge

TL;DR

The paper introduces the Maneuver Identification Challenge, providing a large, complex dataset of flight trajectories to advance AI algorithms for maneuver recognition, safety, and pilot training.

Contribution

It presents a new large-scale dataset and three AI challenges for maneuver classification, including data collection, labeling, and quality assessment.

Findings

Dataset includes thousands of trajectories with detailed flight data.

Constructed using supercomputing resources for data conditioning.

Defines three AI challenges to improve maneuver identification.

Abstract

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.