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Software that contains machine learning algorithms is an integral part ofautomotive perception, for example, in driving automation systems. Thedevelopment of such software, specifically the training and validation of themachine learning components, require large annotated datasets. An industry ofdata and annotation services has emerged to serve the development of suchdata-intensive automotive software components. Wide-spread difficulties tospecify data and annotation needs challenge collaborations between OEMs(Original Equipment Manufacturers) and their suppliers of software components,data, and annotations. This paper investigates the reasons for thesedifficulties for practitioners in the Swedish automotive industry to arrive atclear specifications for data and annotations. The results from an interviewstudy show that a lack of effective metrics for data quality aspects,ambiguities in the way of working, unclear definitions of annotation quality,and deficits in the business ecosystems are causes for the difficulty inderiving the specifications. We provide a list of recommendations that canmitigate challenges when deriving specifications and we propose future researchopportunities to overcome these challenges. Our work contributes towards theon-going research on accountability of machine learning as applied to complexsoftware systems, especially for high-stake applications such as automateddriving.

Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges