Machine Learning for Aviation Safety

Keynote by Dr. Nikunj Oza

The National Airspace, with all of its aircraft, airports, personnel, and related infrastructure, is an incredibly safe system. It is essential that we keep it safe through the changes that it is experiencing, including expected increases in traffic over time, increasing variety of traffic in the form of Unmanned Aerial Vehicles (UAVs), and sharp reductions and increases in traffic due to transient phenomena such as pandemics. The system is currently monitored for safety issues through a set of exceedances, which are rules describing various known safety issues. By definition, these rules cannot identify previously-unknown safety issues. Additionally, they do not identify precursors to these safety issues—states that may not represent safety issues by themselves, but are circumstances under which safety issues are more likely to occur in the near future. In this talk, I describe machine learning-based methods that we have developed for anomaly detection and precursor identification and the aviation safety results that we have obtained. I also describe the active learning algorithm that we have developed to mitigate the false alarm problem that is common to data-driven anomaly detection methods. Our ultimate aim is to allow for aviation safety analysts to discover new safety issues as they arise.