Options companies are pursuing include outsourcing smartphone users and repurposing the technology they're developing in the first place. Even when self-driving vehicles reach production, these processes will be valuable, teaching vehicles how to safely interact in a rapidly changing transportation environment.
"We're very likely going to need some form of data annotation in the long term," Tandon said. "New situations will come up in the future that cars today would not regularly see."
In the past decade, deep-learning algorithms, mobile devices, powerful sensors and graphics processing units — which can evaluate tens of millions of operations in a second — have converged to create a highly capable driving "brain," said Premkumar Natarajan, a computer vision expert at the University of Southern California's Information Sciences Institute.
Though capable, the systems need to learn many situational details, such as driving faster in the left lane or reading pedestrians' body language to know when they are likely to walk into the street.
Engineers "teach" these situations by feeding the computer thousands of images, typically collected via cameras on research vehicles. But these images are relatively meaningless to the computer if objects aren't marked and labeled. The labels help the system differentiate obstacles along a route.
"Ultimately, the computer is acting on what you've fed it," said Daryn Nakhuda, CEO of artificial intelligence training startup Mighty AI in Seattle. "It needs enough of what's right and what's wrong to really understand what it's looking at."
Though the computers can process information many times faster than humans, learning is still gradual and can be thrown off by incorrect or inaccurate information.
"The system slowly learns like the human brain," said Bence Varga, head of European sales at AImotive, a Hungarian startup developing artificial intelligence software for self-driving vehicles. "Everything it sees has to be correctly labeled."
Varga estimated it takes about 100,000 images and a week of teaching for a computer to safely learn a traffic situation. Robust, comprehensive training includes images from around the world and at different times of day, where traffic rules and situations vary widely.
City driving is more labor-intensive than highway driving, Varga said, because of pedestrians and other less-predictable variables.