Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System



Published Sep 4, 2023
Yuning He Johann Schumann


Autonomous Centerline Tracking (ACT) enables an unmanned aircraft to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. The EASA Guidelines for machine-learning based systems list numerous assurance objectives that must be met toward certification and V&V. We extend our analysis framework SYSAI to provide feedback on performance of system and AI component to the designer 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.

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runtime monitoring, assurance and certification, Deep Neural Network, autonomous systems

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