The driving part is usually normal algorithm code and not machine learning though. Machine learning is for object detection, what the software does with that knowledge is regular programming.
Full disclosure, I work for Aurora in the Planning group.
Edit: To clarify, I wonder how you do this part:
> make safe, predictable decisions on the road.
ML models doesn't make predictable outputs. So you always need a system that can handle the ML model going haywire, since you have no way to prove that it wont.
As for stack accuracy and predictability, they're active areas of research for everyone, and part of the secret sauce. There's lots of little mitigations scattered throughout everyone's stacks for various particular issues, but no silver bullet.
So I feel like no matter how many autonomous hours these cars accumulate on the road, they have effectively 0 hours of "learning to drive".
A driving AI has no general knowledge. It doesn’t actually understand what it is looking at.
It’s not possible to make up reasonable responses consistently when you don’t know what you are looking at most of the time, machine or human. Have you ever tried to solve a technical problem before you read the documentation covering the basic concepts? You’re basically throwing shit at the wall until something sticks.
I doubt it, not anything serious at least. Machine learning isn't at the level where it can be used to navigate environments better than human coded algorithms can. Maybe if you had a datacenter worth of computing power to do it, like AlphaStar, but you don't have that in a car. And even if you had that I still doubt it, Alphastar had a perfect representation of the game and could simulate the effects of moves perfectly.