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.
翻译:人工智能(AI)具有巨大的潜力来改进空军的试点培训培训,方法是向试点学员提供关于其操作质量的可操作反馈,并使低成本模拟器的早期受训者能够熟悉无教员驾驶的飞行知识。历史上,由数据、问题描述和示例代码构成的人工智能挑战一直对促进人工智能的突破至关重要。空军-马萨诸塞技术研究所AI A Acclerator(DAF-MIT AI Acceralerator)部利用真实世界空军飞行模拟器数据开发了这种人工智能挑战。Maneuver ID挑战收集了空军下一期飞行员(PTN)收集的数千个虚拟现实模拟飞行器飞行记录。这些数据集已在Maneuver-ID.mit公开发布。这个数据集是首次公开发布美国空军飞行培训数据。利用这一数据集,我们应用了各种人工智能方法来区分“好”和“坏”模拟器数据,并对调整进行分类和定性。这些数据、算算法和软件正在发布,作为生态系统飞行训练的模型性能建立其他飞行的基线。