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.

中文翻译：

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机动识别挑战

从轨迹数据中识别机动的人工智能算法可以在提高飞行安全和飞行员培训方面发挥重要作用。AI 挑战使不同的团队可以共同解决难题，并且是开发 AI 解决方案的有效工具。AI 挑战也是 AI 计算需求的关键驱动因素。机动识别挑战赛在机动 id.mit.edu 上举办，提供了从飞行员在飞行模拟器中练习收集的数千条轨迹、机动描述以及由经验丰富的飞行员执行的这些机动的示例。每个轨迹由位置、速度和飞机方向组成，归一化到一个公共坐标系。数据集的构建需要重要的数据架构来将飞行模拟器日志转换为 AI 就绪数据，其中包括使用超级计算机进行重复数据删除和数据调节。提出了三个挑战。第一个挑战是将物理上合理的（好的）轨迹与不可行的（坏的）轨迹分开。提供了人类标记的好和坏轨迹来帮助完成这项任务。随后的挑战是用预期的动作标记轨迹并评估这些动作的质量。