Waabi says its virtual robotrucks are realistic enough to prove the real ones are safe

Here’s how that works. Whenever its real trucks drive on a highway, Waabi records everything—video, radar, lidar, the state of the driving model itself, and so on. It can rewind that recording to a certain moment and clone the freeze-frame with all the various sensor data intact. It can then drop that freeze-frame into Waabi World and press Play.

The scenario that plays out, in which the virtual truck drives along the same stretch of road as the real truck did, should match the real world almost exactly. Waabi then measures how far the simulation diverges from what actually happened in the real world.

No simulator is capable of recreating the complex interactions of the real world for too long. So Waabi takes snippets of its timeline every 20 seconds or so. They then run many thousands of such snippets, exposing the system to many different scenarios, such as lane changes, hard braking, oncoming traffic and more.  

Waabi claims that Waabi World is 99.7% accurate. Urtasun explains what that means: “Think about a truck driving on the highway at 30 meters per second,” she says. “When it advances 30 meters, we can predict where everything will be within 10 centimeters.”

Waabi plans to use its simulation to demonstrate the safety of its system when seeking the go-ahead from regulators to remove humans from its trucks this year. “It is a very important part of the evidence,” says Urtasun. “It’s not the only evidence. We have the traditional Bureau of Motor Vehicles stuff on top of this—all the standards of the industry. But we want to push those standards much higher.”

“A 99.7% match in trajectory is a strong result,” says Jamie Shotton, chief scientist at the driverless-car startup Wayve. But he notes that Waabi has not shared any details beyond the blog post announcing the work. “Without technical details, its significance is unclear,” he says.

Shotton says that Wayve favors a mix of real-world and virtual-world testing. “Our goal is not just to replicate past driving behavior but to create richer, more challenging test and training environments that push AV capabilities further,” he says. “This is where real-world testing continues to add crucial value, exposing the AV to spontaneous and complex interactions that simulation alone may not fully replicate.”

Even so, Urtasun believes that Waabi’s approach will be essential if the driverless-car industry is going to succeed at scale. “This addresses one of the big holes that we have today,” she says. “This is a call to action in terms of, you know—show me your number. It’s time to be accountable across the entire industry.”