Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
The Prognostics and Health Management Society
2024-10-01
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| Series: | International Journal of Prognostics and Health Management |
| Subjects: | |
| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3860 |
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| Summary: | 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. |
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| ISSN: | 2153-2648 |