Uber, which hasn’t publicly discussed the architecture of its autonomous car platform in great detail, today published a post laying out the technologies that enable engineers within its Advanced Technologies Group (ATG) to test, validate, and deploy AI models to cars. It gives a glimpse into the complexities of self-driving car development generally, and perhaps more importantly, it serves as a yardstick for Uber’s driverless efforts, which suffered a setback following an accident in Tempe, Arizona in May 2018.

According to Uber, the most important component of the ATG’s workflow is VerCD, a set of tools and microservices developed specifically for prototyping self-driving vehicles. It tracks the dependencies among the various codebases, data sets, and AI models under development, ensuring that workflows start with a data set extraction stage followed by data validation, model training, model evaluation, and model serving stages.
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