Yes and no. It's also just nice to have more different kinds of data available. Just because humans don't have laser eyes doesn't mean we can't try to do better than that.
Depth from stereo is not ML, but yes.
Tesla in particular, and others are moving towards getting depth from ML. Yes, you can do dumb coincidence-finding, but there's a lot of corner cases (leafy objects, specular reflections, etc) that screw this up. Humans don't just use coincidence finding, but use all kinds of other clues (size, texture gradients, monocular parallax, shadows, linear perspective, attenuation from haze, etc) to infer depth.
The human neural network is of course fine tuned to this and my father who is blind on one eye will actually move his head sideways a bit to judge distance when he is driving :)
Maybe not just, but we do use it, except we do it by converging our eyes for _actual_ coincidence within a small focal center instead of ambiguous coincidence everywhere in the field of view like CV stereo typically tries to do. Gimbaled cameras that (metaphorically speaking, of course) "look" where drivers are supposed to look with an attention model instead of being statically coplanar could do this too.
Only at very close range. That's convergence, and mostly a 5m and less phenomenon (not so relevant at driving ranges).
not amazingly well, though, because there are millions of victims each year
SDCs are supposed to be better than us, so more sensors make sense.
Also unsafe lefts and ran/stale lights. That's probably most all of them between our comments.