Jay Gierak of Ghost, based in Mountain View, California, is impressed by Wayve’s demos and agrees with the company’s overall view. “The robotics approach is not the right way to do this,” says Gierak.
But he’s not convinced of Wayve’s full commitment to deep learning. Instead of a single large model, Ghost trains hundreds of smaller models, each with a specialty. Then he hand-codes simple rules that tell the autonomous driving system which models to use in which situations. (Ghost’s approach is similar to that taken by another AV2.0 company, Israel-based Autobrains. But Autobrains uses another layer of neural networks to learn the rules.)
According to Volkmar Uhlig, co-founder and CTO of Ghost, breaking down AI into many smaller pieces, each with specific functions, makes it easy to establish that an autonomous vehicle is safe. “At some point, something will happen,” he says. “And a judge will ask you to sign the code that says: ‘If there is a person in front of you, you have to stop.’ That code snippet must exist.” The code can still be learned, but in a large model like Wayve’s it would be hard to find, says Uhlig.
Still, the two companies pursue complementary goals: Ghost wants to make consumer vehicles that can drive themselves on highways; Wayve wants to be the first company to put driverless cars in 100 cities. Wayve is now working with UK grocery giants Asda and Ocado, collecting data from their urban delivery vehicles.
However, by many measures, both companies lag far behind the market leaders. Cruise and Waymo have logged hundreds of hours of driving without humans in their cars and already offer robotaxi services to the public in a small number of locations.
“I don’t want to diminish the scale of the challenge ahead of us,” says Hawke. “The audiovisual industry teaches you humility.”