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How to Train a Self-Driving Car

How to Train a Self-Driving Car

We humans tend to take for granted all the complexities of driving a car. Driverless vehicles don’t have that luxury—the ability to view and immediately process a scene just isn’t intuitive to a machine.

When it comes to autonomous driving, humans have to teach cars to understand the differences between a pedestrian, a bus, and a street sign. And they have to do it across different geographies, in various climates, with road signs written in multiple languages. When we’re talking about an industry where people’s lives are on the line, we need the highest-quality training data.

One of our most popular posts here on the Mighty AI blog was The Anatomy of a Good Annotator for AI Training Data. For a quick recap, here are the four main categories to consider for computer vision and natural language applications:

  • Demographics: Geographic location, age, gender, marital status, education, income, political affiliation, and more.
  • Language: Proficiency, grammar rules (even the sneaky ones), slang terms, and dialects.
  • Speciality: People who have the domain specialty to understand topical nuances.
  • Skill: Proven experience labeling specific data quickly and accurately.

These requirements are as important to remember when training data for autonomous driving as they are for any AI model. Take a look at how we’re training data to help keep roads safe with our annotation process for autonomous driving tasks.

Ready to learn more about autonomous driving training data? Check out our suite of annotation tools here.

 

image credit: libreshot.com