[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124719-en":3,"doc-seo-124719-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124719,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","Runway Sign Classifier - A DAL C Certifiable Machine Learning System","Machine Learning (ML) can enable advanced automation in aviation, yet certification standards remain incompatible with key ML needs such as traceability, explainability, and adequate coverage metrics. Certification therefore becomes problematic for safety-critical airborne systems. This paper presents a case study of an airborne runway sign detection and classification system built on a DNN, extending prior DAL “D” compliance to DAL “C”. It uses two redundant, dissimilar DNNs and ML-specific data management to illustrate how ML certification challenges can be mitigated for medium criticality.","Runway Sign Classifier: A DAL C Certifiable Machine Learning System  \narXiv :2310 .06506v1 [ cs .LG] 10 Oct 2023  \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 [florian.holzapfel@tum.de](florian.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] . 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 [2],[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 applica","cbCaikbUR4II34XY","https://ap.wps.com/l/cbCaikbUR4II34XY","pdf",554480,1,8,"English","en",105,"# Abstract\n# Introduction and Related Work","[{\"question\":\"Why is certifying ML-based airborne systems difficult under existing standards?\",\"answer\":\"Existing standards struggle with ML traceability and explainability, and traditional coverage metrics do not adequately fit ML verification needs.\"},{\"question\":\"What airborne system does the paper study?\",\"answer\":\"It studies a runway sign detection and classification system that uses a deep neural network (DNN) for airport sign recognition.\"},{\"question\":\"How does the work extend compliance from DAL “D” to DAL “C”?\",\"answer\":\"It upgrades the system to meet the stricter DAL “C” requirements by using two redundant and dissimilar DNNs plus ML-specific data management techniques.\"}]","Runway Sign Classifier - A DAL C Certifiable Machine Learning System | PDF",1785894088,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"runway-sign-classifier-a-dal-c-certifiable-machine-learning-system","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/runway-sign-classifier-a-dal-c-certifiable-machine-learning-system/124719/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is certifying ML-based airborne systems difficult under existing standards?","Question",{"text":75,"@type":76},"Existing standards struggle with ML traceability and explainability, and traditional coverage metrics do not adequately fit ML verification needs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What airborne system does the paper study?",{"text":80,"@type":76},"It studies a runway sign detection and classification system that uses a deep neural network (DNN) for airport sign recognition.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the work extend compliance from DAL “D” to DAL “C”?",{"text":84,"@type":76},"It upgrades the system to meet the stricter DAL “C” requirements by using two redundant and dissimilar DNNs plus ML-specific data management techniques.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":29,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":29,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]