In this tutorial, we present a portion of unique industry experience in efficient data annotation (labelling) for self-driving cars shared by both leading researchers and engineers from Yandex.

We will present a data processing pipeline required for the cars to learn how to behave autonomously on the roads and we will also show how data annotation constitutes a crucial part that makes the learning process effective. This will be followed by an introduction to data annotation via public crowdsourcing marketplaces and a presentation of key components of efficient annotation (the technique of task decomposition, quality control methods, aggregation, incremental relabelling, etc). We will study the most popular and important crowdsourcing tasks needed for self-driving cars development. Then, in a practice session, participants of our tutorial will choose one of the real annotation tasks, experiment with selecting settings for the labelling process, and launch their annotation project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session. Finally, participants will receive a feedback about their projects and practical advice to make them more efficient.