Active Learning, Data Selection, Data Auto-Labeling, and Simulation in Autonomous Driving — Part 6

Isaac Kargar
10 min readFeb 14, 2024

Let’s go for Waabi!

Waabi

In a recent workshop on self-supervised learning for autonomous driving at ICCV 2021, Raquel Urtasun talked about their labeling mechanisms at Waabi. She mentions that there is no need for humans in the labeling loop and it is possible to make the entire loop automatic.

Here is the Autonomy workflow used at Waabi:

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We have access to a fleet of vehicles as well as data collection platforms. So while we can collect a large amount of data, labeling it all is prohibitively expensive. Change and evolution of datasets, on the other hand, is necessary and occurs frequently in industry and the real world. However, because the world is changing as we drive to different cities, seeing different scenes and situations, and the city changing due to, for example, constructions, we need to change our datasets and train our models on them in order to be able to handle the situations that we see and cannot handle. Annotating these datasets is costly and the solution for that can be data curation.

In order to select samples in data to label, there are several techniques.

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Interesting

We can choose a data point that we believe is interesting, exciting, and would be beneficial to learn about, whether for training or testing purposes.

They have some measures in place to select data from the logs and data collected by each of the vehicles. They accomplish this by using an intermediate process to tag logs with various properties, which they can then use to generate various notions of what might be interesting. They then rank and select the best examples. Automation is critical in this process.

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While humans are capable of tagging, the process must also be automated. They’ve devised two distinct methods for…

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Isaac Kargar

Co-Founder and CIO @ Resoniks | Ph.D. candidate at the Intelligent Robotics Group at Aalto University | https://kargarisaac.github.io/