> 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".
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.