论文标题
通过机动识别挑战，AI启用了操纵识别
AI Enabled Maneuver Identification via the Maneuver Identification Challenge
论文作者
论文摘要
人工智能（AI）通过向试验者提供可行的反馈来提高空军飞行员训练的巨大潜力，并在低成本模拟器中为早期阶段的学员提供了无人驾驶飞行的质量，并使无教练的飞行熟悉。从历史上看，包括数据，问题描述和示例代码组成的AI挑战对于加强AI突破至关重要。空军马萨诸塞州技术学院AI加速器（DAF-MIT AI加速器）使用现实世界中空军飞行模拟器数据开发了这样的AI挑战。机动ID挑战赛召集了成千上万的虚拟现实模拟器飞行记录，由实际空军学生飞行员在下一步飞行员培训（PTN）收集。该数据集已在maneuver-id.mit.edu上公开发布，它代表了USAF飞行培训数据的第一个公开发布。使用此数据集，我们应用了各种AI方法来分开“好”与“坏”模拟器数据，并对操作进行分类和表征。这些数据，算法和软件正在作为模型性能的基线发布，以供其他人构建以实现AI生态系统进行飞行模拟器培训。
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.
