> 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.

> Humans don't just use coincidence finding

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.

> except we do it by converging our eyes for _actual_ coincidence within our focal center instead of ambiguous coincidence everywhere in the field of view.

Only at very close range. That's convergence, and mostly a 5m and less phenomenon (not so relevant at driving ranges).

Tesla is using data from the radar to annotate the images from the cameras with distance before running it through the ML training, but for driving it is using a combination of all (stereo vision, ML and radar) to produce the final number.

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 :)

Yah. Waymo is doing the same with the lidar, which provides both higher accuracy and higher resolution "ground truth" distance data. And yes, everyone uses sensor fusion and filtering, not just "ML".

> we still manage to drive

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.

I'd argue that the majority of victims are the result of humans suppressing their judgment. E.g. not because a human was incapable to drive safely, but purposedly chose to drive unsafely: overtake where it is limited visibility, speeding when conditions do not allow for it.

Attention-related crashes are likely a good chunk as well.

Also unsafe lefts and ran/stale lights. That's probably most all of them between our comments.

It isn't "necessary" for small values of "necessary", ie if you have a human brain hooked up to human eyes. It is extremely necessary if you only have a computer and some primitive software, like we do now, as in "self-driving won't work as well otherwise".

Yet depth from stereo is, to some extent, AI because the matching of pixels that corresponds to the same physical point is not trivial and uses heuristics.

Depth from stereo gets worse at distance, while LIDAR and RADAR read depth directly.