[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-240922-105":53,"doc-detail-240922-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","runway-sign-classifier-dal-c-certifiable-machine-learning-system-case-study","Runway Sign Classifier - DAL C Certifiable Machine Learning System - Case Study","","Machine learning advances are enabling new automation possibilities for aviation, yet ML-based airborne systems face incompatibilities with existing certification standards, including traceability, explainability, and inadequate coverage metrics. Certification under current standards is therefore problematic. This paper presents a case study of an airport runway sign detection and classification system using deep neural networks, extending prior work from Design Assurance Level “D” to the stricter Design Assurance Level “C”.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/runway-sign-classifier-dal-c-certifiable-machine-learning-system-case-study/240922/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/runway-sign-classifier-dal-c-certifiable-machine-learning-system-case-study/240922.png","ImageObject",442,249,{"name":88,"@type":89},"Jake","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-28","2026-09-11",true,{"@type":98,"interactionType":99,"userInteractionCount":8},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why is certifying ML-based airborne systems difficult under existing standards?","Question",{"text":108,"@type":109},"Because ML models raise major issues such as traceability and explainability, and traditional coverage metrics do not adequately fit ML implementations.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What system does the case study focus on?",{"text":113,"@type":109},"An airborne system for airport runway sign detection and classification using a deep neural network computer-vision approach.",{"name":115,"@type":106,"acceptedAnswer":116},"How is the work extended from DAL “D” to DAL “C”?",{"text":117,"@type":109},"The paper upgrades the prior system to meet DAL C by applying an architectural mitigation technique using two redundant and dissimilar deep neural networks, supported by ML-specific data management techniques.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},240922,1791739224,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":8,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":76},962084928904,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Runway Sign Classifier: A DAL C Certifiable Machine Learning System  \nKonstantin Dmitriev  \nTechnical University of Munich Garching, Germany [konstantin.dmitriev@tum.de](konstantin.dmitriev@tum.de)  \nJohann Schumann  \nKBR, NASA Ames Research Center Moffett Field, CA [johann.m.schumann@nasa.gov](johann.m.schumann@nasa.gov)  \nIslam Bostanov  \nTechnical University of Munich Garching, Germany [islam.bostanov@tum.de](islam.bostanov@tum.de)  \nMostafa Abdelhamid  \nTechnical University of Munich Garching, Germany mostafa.abdelhamid@tum.de  \nFlorian Holzapfel  \nTechnical University of Munich Garching, Germany fl[orian.holzapfel@tum.de](orian.holzapfel@tum.de)  \nAbstract—In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous operation of large commercial airplanes. However, ML technology faces major incompatibilities with existing airborne certification standards, such as ML model traceability and explainability issues or the inadequacy of traditional coverage metrics. Certification of ML-based airborne systems using current standards is problematic due to these challenges. This paper presents a case study of an airborne system utilizing a Deep Neural Network (DNN) for airport sign detection and classification. Building upon our previous work, which demonstrates compliance with Design Assurance Level (DAL)”D”, we upgrade the system to meet the more stringent requirements of Design Assurance Level ”C”. To achieve DAL C, we employ an established architectural mitigation technique involving two redundant and dissimilar Deep Neural Networks. The application of novel ML-specific data management techniques further enhances this approach. This work is intended to illustrate how the certification challenges of ML-based systems can be addressed for medium criticality airborne applications.  \nI. INTRODUCTION AND RELATED WORK  \nThe remarkable progress of Machine Learning (ML) technologies in recent years has the potential to revolutionize aviation [1], [2] . Data-driven ML systems can implement highly complex cognitive functions such as vision and language processing that can enable single pilot or fully autonomous operation of large commercial airplanes, a level of automation not possible with traditional rule-based software systems [3] . However, ML technology encounters various inherent incompatibilities with existing airborne certification standards. These incompatibilities encompass challenges related to explainability, traceability, and implementation coverage [4] . As a result, the utilization of ML-based applications within the framework of existing airborne certification standards is currently impeded.  \nThe industry, aviation authorities, and academia are actively engaged in collaborative efforts to develop new certification standards aimed at addressing the existing incompatibilities  \nof ML technology with existing certification practices. The development of a new standard for airborne machine learning (ML) certification has been underway since 2019 through the collaborative efforts of the EUROCAE/SAE WG-1141/G-342 joint working group. The working group has published reports reflecting the intermediate results [4], [5] . The European Aviation Safety Agency (EASA) has released the guidance for ML applications [8], which includes anticipated objectivesand means of compliance for the certification of safety-critical airborne systems based on machine learning. In collaboration with Daedalean AG, the U.S. Federal Aviation Administration (FAA) has released a research report on a neural network vision-based landing guidance system [6] . This report offers a practical assessment of the previously proposed W-shaped process for ML applications, as outlined in earlier Daedalean AG publications [1], [","cbCaiqGlyyZ6KCKX","https://ap.wps.com/l/cbCaiqGlyyZ6KCKX","pdf",487920,8,"English","# Abstract\n# Introduction and Related Work\n## Certification challenges for ML-based airborne systems\n## Emerging efforts and guidance for ML certification\n## Proposed DAL C extension and architectural mitigation","[{\"question\":\"Why is certifying ML-based airborne systems difficult under existing standards?\",\"answer\":\"Because ML models raise major issues such as traceability and explainability, and traditional coverage metrics do not adequately fit ML implementations.\"},{\"question\":\"What system does the case study focus on?\",\"answer\":\"An airborne system for airport runway sign detection and classification using a deep neural network computer-vision approach.\"},{\"question\":\"How is the work extended from DAL “D” to DAL “C”?\",\"answer\":\"The paper upgrades the prior system to meet DAL C by applying an architectural mitigation technique using two redundant and dissimilar deep neural networks, supported by ML-specific data management techniques.\"}]","Runway Sign Classifier - DAL C Certifiable Machine Learning System - Case Study | PDF",1789167579]