Exponential. It gives range and shape data, which a pure-optical system needs to infer from a 2D image. This kind of image processing is still an open problem in ML.

The usual metric for self-driving car success is "disengagements per mile", ie how frequently a driver needs to intervene to avoid a crash. From my anecdotal readings of Tesla Autopilot reviews, it's on the order of 0.1 per mile. For Waymo and Cruise, it's on the order of 0.01 per THOUSAND miles. That's a very different definition of "driver" than the one that Tesla Autopilot requires.

I don't know the total number of miles on all Teslas on Autopilot, but it has had much more than one accident.

EDIT: and that Waymo crash was not a self-driving error; it was T-boned by a human-driven car running a red light.

Fair, depth perception from 2D isn’t there yet through ML, but mixed with radar can it be effective enough.

The reason I assumed it works is that lidar on the article above seems more like a redundancy. Because their camera system have the short range covered and radar has the long range covered. Lidar seems to augment over it.

Though the order of disengagement is a great stat, that definitely shows how much better waymo is compared to Tesla

> but mixed with radar can it be effective enough.

The usual solution is actually lidar + optical; lidar gives much better spatial resolution than radar, which is why it's been the standard going back to the DARPA challenge. You really want to have good spatial resolution in order to distinguish e.g. bikers and tail-lights and road signs for your optical systems, which radar typically isn't good enough for; that's the point of that qualifier in "imaging radar". Still probably worse performance (i.e. time and spatial resolution) than lidar, but better range and weather resistance.

(The previous generation of Waymo cars already had one lidar on top; the radar and the close-range lidars are the new additions.)