I forget who it was (maybe George Hotz) that said something to the effect of "All bad drivers are bad in different ways, but all good drivers are good in the same way".
The point being made was basically that in the aggregate you can more or less generalize to something like "the tall part of the bell curve is good driving and everything on the tails should be ignored".
Since learning happens in aggregate (individual cars don't learn – they simply feed data back to the mothership), your example of a single car errantly turning the wheel to adjust the radio would fall into the "bad in different ways" bucket and it would be ignored.
"All bad drivers are bad in different ways, but all good drivers are good in the same way".
I accept that as a plausible hypothesis to work off of and see how far it goes, but I would not bank on it as truth.
I'll give another example, I think a significant portion of the time, people roll through stop signs (we'll say, 25% of the time? intuitive guess). I do it myself quite often. This is because not all intersections are built the same - some intersections have no obstacles anywhere near them and you can tell that duh, there's no cars coming up at the same time as me. Other intersections are quite occluded by trees and what not.
I'm fine with humans using judgement on these, but I would not trust whatever the statistical machine ends up generalizing to. I do not think rolling a stop sign makes you a 'bad' driver (depending on the intersection). Still, if I knew I was teaching a machine how to drive, I would not want it to be rolling stop signs.
That sounds like a complicated way to say there are more ways to screw up than do it perfectly, which, duh.
Not to discount this at all, but... yea
Even if the brains of it become perfect, I doubt the vision-only approach (or has that changed?)
They need at least somewhat decently consistent 'signal' to act appropriately... and there are some mornings I just don't drive because visibility is so poor
The point being made was basically that in the aggregate you can more or less generalize to something like "the tall part of the bell curve is good driving and everything on the tails should be ignored".
Since learning happens in aggregate (individual cars don't learn – they simply feed data back to the mothership), your example of a single car errantly turning the wheel to adjust the radio would fall into the "bad in different ways" bucket and it would be ignored.