UK startup Wayve believes trial-and-error machine learning, much like the way humans learn, is the key to autonomous cars(Credit: Wayve) A pair of artificial intelligence Ph.Ds from Cambridge University are going all-in on machine learning as the foundation of autonomous cars. Their company, Wayve, has just released video of a kitted-out Renault Twizy teaching itself to follow a lane from scratch, over the course of about 20 minutes.

Wayve’s Amar Shah and Alex Kendall believe there’s been too much hand-engineering going on as people try to solve the self-driving car problem. “The missing piece of the self-driving puzzle is intelligent algorithms, not more sensors, rules and maps,” says Shah, Wayve co-founder and CEO.

“Humans have a fascinating ability to perform complex tasks in the real world, because our brains allow us to learn quickly and transfer knowledge across our many experiences. We want to give our vehicles better brains, not more hardware.”

With that approach in mind, the team took a Renault Twizy, kitted out with a single camera on the front and modified with the ability to computer-operate the steering, gas and brakes. They hooked it up to a graphics processing unit capable of intelligently analyzing the camera data in real time, and ran a learning program based on experimentation, optimization and evaluation.

They put the Twizy on a narrow, gently curving lane. A human driver sat in the driver’s seat, then handed full control over to the car, not telling it what its task was, and let it experiment with the controls. Read more from…

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