  [Context and motivation] For automated driving systems, the operational
context needs to be known in order to state guarantees on performance and
safety. The operational design domain (ODD) is an abstraction of the
operational context, and its definition is an integral part of the system
development process. [Question / problem] There are still major uncertainties
in how to clearly define and document the operational context in a diverse and
distributed development environment such as the automotive industry. This case
study investigates the challenges with context definitions for the development
of perception functions that use machine learning for automated driving.
[Principal ideas/results] Based on qualitative analysis of data from
semi-structured interviews, the case study shows that there is a lack of
standardisation for context definitions across the industry, ambiguities in the
processes that lead to deriving the ODD, missing documentation of assumptions
about the operational context, and a lack of involvement of function developers
in the context definition. [Contribution] The results outline challenges
experienced by an automotive supplier company when defining the operational
context for systems using machine learning. Furthermore, the study collected
ideas for potential solutions from the perspective of practitioners.
