Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System - Yuning He; Johann Schumann (International Journal of Prognostics and Health Management)
Autonomous Centerline Tracking (ACT) enables an uninhabited aircraft system (UAS) to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. Guidelines by the European Union Aviation Safety Agency (EASA) for machine-learning based systems list numerous assurance objectives that must be met toward Verification and Validation (V&V), and certification. We extend our analysis framework SYSAI (System Analysis using Statistical AI) to support meeting assurance objectives for a system with AI/ML (Artificial Intelligence / Machine Learning) components and describe a combination with a runtime monitoring architecture that also supports advanced risk mitigation to support safety assurance of a complex AI-based aerospace system.