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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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2024-10-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3860 |
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| _version_ | 1850280323100704768 |
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| author | Yuning He Johann Schumann |
| author_facet | Yuning He Johann Schumann |
| author_sort | Yuning He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2015353fe3e44e589f9edf636d68663c |
| institution | OA Journals |
| issn | 2153-2648 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-2015353fe3e44e589f9edf636d68663c2025-08-20T01:48:48ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482024-10-01153110https://doi.org/10.36001/ijphm.2024.v15i3.3860Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking SystemYuning He0Johann Schumann1NASA Ames Research CenterKBR/Wyle, NASA ARCAutonomous 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.https://papers.phmsociety.org/index.php/ijphm/article/view/3860complex safety-critical systemsafe aistatistical v&v framework |
| spellingShingle | Yuning He Johann Schumann Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System International Journal of Prognostics and Health Management complex safety-critical system safe ai statistical v&v framework |
| title | Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System |
| title_full | Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System |
| title_fullStr | Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System |
| title_full_unstemmed | Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System |
| title_short | Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System |
| title_sort | statistical analysis and runtime monitoring for an ai based autonomous centerline tracking system |
| topic | complex safety-critical system safe ai statistical v&v framework |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/3860 |
| work_keys_str_mv | AT yuninghe statisticalanalysisandruntimemonitoringforanaibasedautonomouscenterlinetrackingsystem AT johannschumann statisticalanalysisandruntimemonitoringforanaibasedautonomouscenterlinetrackingsystem |