Safety Assessment of a Machine Learning-Based Runway Detector

Xwing proposes to develop a black-box verification framework to facilitate the certification of safety-critical machine learning subsystems. Xwing, a leader in the development of autonomous flight technology, is investigating the safety assessment of machine learning models. Machine learning has demonstrated impressive results in sensing tasks relevant to autonomous flight. One such example is improving the safety and performance of runway detection in camera images. However machine learning cannot be substantiated following the standard DO-178 framework for software. Xwing thus proposes to investigate how to implement IASMS for the safety assessment of machine learning based subsystems. By leveraging an in-house simulator and runway detector, Xwing will investigate efficient sampling methods to estimate the probability of failure of the subsystem. The proposal focuses on three of the IASMS pillars: assessing the safety of the current model, mitigating the failures modes exhibited through the framework, and assuring a revised model for certification. The framework will be validated with flight test data and used to produce example certification artifacts.

Pathfinding for Airspace with Autonomous Vehicles (PAAV): directly addresses the auto-land challenge of the PAAV by performing the safety assessment Advanced Air Mobility (AAM): the economics of AAM truly unlock through autonomy, and the certification of machine learning algorithms is a key milestone High Density Vertiplex (HDV): the safety assessment of autonomous landing on HDV landing pads could be performed through this proposal System-Wide Safety (SWS): general modernization of aircraft technologies, including through machine learning

In general, the autonomous flight industry is looking for suggestions of generic certification methodologies for machine learning models. Such a proposal would benefit the whole autonomous flight industry and has the potential to unlock a plethora of new applications.

This is early/mid-stage (TRL 3) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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