Exceptional advances of Machine Learning (ML) technologies in recent years have opened up opportunities for next level of automation in aviation systems, such as single pilot or fully autonomous operation of large commercial airplanes. But there are several essential incompatibilities of Machine Learning technology with existing airborne certification standards, such as traceability and coverage issues. These incompatibilities prevent approval of ML-based applications using current certification standards. In this paper, we study the combination of architectural mitigation technique with several ML-specific verification methods to achieve compliance with Design Assurance Level (DAL) C. This approach proposes incremental evolution of existing assurance practices and extends the custom ML workflow for DAL D systems presented in our previous works [1], [2].


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Towards Design Assurance Level C for Machine-Learning Airborne Applications


    Beteiligte:


    Erscheinungsdatum :

    18.09.2022


    Format / Umfang :

    929814 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Toward Design Assurance of Machine-Learning Airborne Systems

    Dmitriev, Konstantin / Schumann, Johann / Holzapfel, Florian | TIBKAT | 2022


    Toward Design Assurance of Machine-Learning Airborne Systems

    Dmitriev, Konstantin / Schumann, Johann / Holzapfel, Florian | AIAA | 2022


    Evaluation of New Assurance Tools for Airborne Machine Learning-Based Functions

    Liu, Cong / Herencia-Zapana, Heber / Hasan, Saqib et al. | IEEE | 2024


    Managing Criticality of Airborne Separation Assurance Systems Applications

    Zeitlin, A. D. / Bonnemaison, B. | British Library Conference Proceedings | 2001


    Managing Criticality of Airborne Separation Assurance Systems Applications

    Zellweger, Andres G. / Donohue, George L. | AIAA | 2001