Safe Aviation Autonomy with Learning-enabled Components in the Loop
Future autonomous aviation systems, such as cyber-taxis, are expected to complete millions of flights per day. These systems have the potential to significantly benefit from machine-learning-enabled components for enhanced perception, decision-making, and control that outperform their traditional, non-learning based counterparts. Despite the promise of deploying machine learning (ML) in future aviation systems, today’s ML methods remain poor at generalizing to unseen conditions and lack formal safety guarantees. Our goal is to develop safe, trustworthy, and robust ML methods that will usher in a new level of autonomy in the national airspace.