  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.
