TY - GEN
T1 - Safety Assessment of a Machine Learning-Based Aircraft Emergency Braking System
T2 - 43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024
AU - Dmitriev, Konstantin
AU - Rhein, Julian
AU - Beller, Lukas
AU - Bröcker, Johannes
AU - Huber, Evangelos
AU - Schumann, Johann
AU - Holzapfel, Florian
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Machine Learning (ML) is revolutionizing many technological fields, but its use in aviation remains restricted due to stringent certification requirements. Efforts by the aviation community to establish standards for certifying ML-based systems are progressing, yet challenges persist, particularly with safety assessment methods for ML-based systems. This research addresses these challenges through a case study of an autonomous emergency braking system utilizing a computer vision deep neural network (DNN). We demonstrate a safety assessment process tailored to ML-specific concerns, such as low integrity and performance variability in quantitative safety analysis. This study can serve as an illustrative example to facilitate the discussion and convergence on certification aspects for ML-based systems within the aviation community.
AB - Machine Learning (ML) is revolutionizing many technological fields, but its use in aviation remains restricted due to stringent certification requirements. Efforts by the aviation community to establish standards for certifying ML-based systems are progressing, yet challenges persist, particularly with safety assessment methods for ML-based systems. This research addresses these challenges through a case study of an autonomous emergency braking system utilizing a computer vision deep neural network (DNN). We demonstrate a safety assessment process tailored to ML-specific concerns, such as low integrity and performance variability in quantitative safety analysis. This study can serve as an illustrative example to facilitate the discussion and convergence on certification aspects for ML-based systems within the aviation community.
UR - http://www.scopus.com/inward/record.url?scp=85211217355&partnerID=8YFLogxK
U2 - 10.1109/DASC62030.2024.10749696
DO - 10.1109/DASC62030.2024.10749696
M3 - Conference contribution
AN - SCOPUS:85211217355
T3 - AIAA/IEEE Digital Avionics Systems Conference - Proceedings
BT - DASC 2024 - Digital Avionics Systems Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 September 2024 through 3 October 2024
ER -