TY - GEN
T1 - Towards Design Assurance Level C for Machine-Learning Airborne Applications
AU - Dmitriev, Konstantin
AU - Schumann, Johann
AU - Holzapfel, Florian
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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].
AB - 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].
UR - http://www.scopus.com/inward/record.url?scp=85141937397&partnerID=8YFLogxK
U2 - 10.1109/DASC55683.2022.9925741
DO - 10.1109/DASC55683.2022.9925741
M3 - Conference contribution
AN - SCOPUS:85141937397
T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings
BT - 2022 IEEE/AIAA 41st Digital Avionics Systems Conference, DASC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022
Y2 - 18 September 2022 through 22 September 2022
ER -