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Quality Ground Truth Labels for All Autonomous Driving Applications

Quality Ground Truth Labels for All Autonomous Driving Applications

Building Quality Models for Autonomous Driving

This ebook aims to give you a deeper understanding of what “quality” might mean for your autonomous vehicle use case, and how you can start measuring it. You’ll learn how you can effectively balance time versus quality — starting small, iterating, and scaling slowly but deliberately — to set your organization up for the quickest past to accuracy.

Is Your Training Data Veering You Off Course?

Good annotation and testing practices are the foundations of building a great model. But understanding what constitutes quality data is a tricky question. While “as accurate as possible” seems the obvious answer, this thinking can bog down any team in an endless cycle of manual annotation, training, more annotation, and retraining.

What’s inside

  • An explainer on why managing noise is critical for autonomous driving models (and which noise to safely ignore)
  • How to set data quality requirements that meet your needs
  • How to define and measure data quality for annotation
  • How to guarantee data quality

About the authors

Lead Product Manager

Renata Walker

Product Manager

Patrick Steeves