Scaling the Data Loop: Accelerating the Development and Testing of Autonomous Vehicles

Abstract

The development and testing of autonomous vehicles requires a massive edge-to-cloud-to-edge “data loop” that begins & ends with the test fleet. In order to meet customer sprint cycle KPIs and iterate across the data loop quickly & efficiently, Microsoft solved a series technical and logistical challenges at scale across an end to end workflow featuring sensor reprocessing, CI/CD & ML pipeline management, and both perception and post-perception closed loop simulation.The focus of this presentation will be a high-level description of the entire data loop with a focus on the unique challenges of sensor reprocessing (commonly called “re-sim” or “playback”) at a massive scale. Re-sim is simultaneously a logistical challenge, a safety challenge, and a budget challenge. The presentation will explain how Microsoft Azure:• Extracts petabytes of data (daily!) from both stationary & nomadic fleets dispersed globally• Filters, processes, & curates those petabytes of data• Accurately assesses & recreates the real-world performance of a device under test• … and do all of the above quickly and cheaplyThe presentation will conclude with a forward-looking overview of anticipated future challenges Microsoft will need to solve as the technology continues to evolve and customer KPIs grow more sophisticated.