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[Context and motivation] For automated driving systems, the operationalcontext needs to be known in order to state guarantees on performance andsafety. The operational design domain (ODD) is an abstraction of theoperational context, and its definition is an integral part of the systemdevelopment process. [Question / problem] There are still major uncertaintiesin how to clearly define and document the operational context in a diverse anddistributed development environment such as the automotive industry. This casestudy investigates the challenges with context definitions for the developmentof perception functions that use machine learning for automated driving.[Principal ideas/results] Based on qualitative analysis of data fromsemi-structured interviews, the case study shows that there is a lack ofstandardisation for context definitions across the industry, ambiguities in theprocesses that lead to deriving the ODD, missing documentation of assumptionsabout the operational context, and a lack of involvement of function developersin the context definition. [Contribution] The results outline challengesexperienced by an automotive supplier company when defining the operationalcontext for systems using machine learning. Furthermore, the study collectedideas for potential solutions from the perspective of practitioners.

Setting AI in context: A case study on defining the context and operational design domain for automated driving