Certifiability analysis of machine learning systems for low-risk automotive applications

Machine learning (ML) is increasingly employed for automating complex tasks, specifically in autonomous driving. While ML applications bring us closer to fully autonomous systems, they simultaneously introduce security and safety risks specific to safety-critical systems. Existing methods of software development and systems based on ML are fundamentally different. Moreover, the existing certification methods for automotive systems cannot fully certify the safe operation of ML-based components and subsystems. This is because existing safety certification criteria were formulated before the advent of ML. Therefore, new or adapted methods are needed to certify ML-based systems. This article analyses the existing safety standard, ISO26262, for automotive applications, to determine the certifiability of ML approaches used in low-risk automotive applications. This will contribute towards addressing the task of assuring the security and safety of ML-based autonomous driving systems, particularly for low-risk automotive applications, to gain the trust of regulators, certification agencies, and stakeholders.

URI

http://hdl.handle.net/10454/19992

Version

Accepted manuscript

Citation

Vasudevan V, Abdullatif A, Kabir S et al (2024) Certifiability analysis of machine learning systems for low-risk automotive applications. Computer. 57(9): 45-56.

Link to Version of Record

https://doi.org/10.1109/MC.2024.3401402

Type

Article