Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
This work addresses practical collaboration problems for OEMs and suppliers in the automotive industry, but it is incremental as it builds on existing research on accountability in machine learning.
The paper investigates challenges in specifying data and annotation needs for automotive perception software, finding that issues like ineffective metrics, ambiguous workflows, and ecosystem deficits cause difficulties, and provides recommendations to mitigate these problems.
Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, require large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify 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 these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study 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 in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.