  The use of machine learning (ML) is on the rise in many sectors of software
development, and automotive software development is no different. In
particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving
Systems (ADS) are two areas where ML plays a significant role. In automotive
development, safety is a critical objective, and the emergence of standards
such as ISO 26262 has helped focus industry practices to address safety in a
systematic and consistent way. Unfortunately, these standards were not designed
to accommodate technologies such as ML or the type of functionality that is
provided by an ADS and this has created a conflict between the need to innovate
and the need to improve safety. In this report, we take steps to address this
conflict by doing a detailed assessment and adaption of ISO 26262 for ML,
specifically in the context of supervised learning. First we analyze the key
factors that are the source of the conflict. Then we assess each software
development process requirement (Part 6 of ISO 26262) for applicability to ML.
Where there are gaps, we propose new requirements to address the gaps. Finally
we discuss the application of this adapted and extended variant of Part 6 to ML
development scenarios.
