Certifiability Analysis of Machine Learning Systems for Low-Risk Automotive Applications
We analyze the existing safety standard, ISO 26262, for automotive applications, determining the certifiability of machine learning (ML) approaches used in low-risk automotive applications. This can help assuring the security and safety of ML-based autonomous driving systems, gaining the trust of regulators, certification agencies, and stakeholders.
Authors
- Felician Campean (ORCID: https://orcid.org/0000-0003-4166-8077)
- Vinod Vasudevan (ORCID: https://orcid.org/0000-0002-5147-5239)
- Sohag Kabir (ORCID: https://orcid.org/0000-0001-7483-9974)
- Amr Abdullatif (ORCID: https://orcid.org/0000-0003-1801-4673)
Institutions
- Jaguar Land Rover (United Kingdom) (GB)
- University of Bradford (GB)
Publication Details
- Journal
- Computer
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1109/mc.2024.3401402
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00