There's a serious problem at the heart of "throw a bunch of data at it" strategy - driving has an extremely long tail that's relatively fat. You can hit the "average case" relatively quickly (you can assume that Google's done that for the SoCal environment), but being able to extend that dataset to other, less likely scenarios, like "heavy downpour in SF" or "freak snowstorm" or "sudden construction due to water main break" is really hard.
None of what you said has anything to do with my point that the pre-mapping and then LIDAR-localization approach taken by Google is the only approach shown to work.
You're making a point about end-to-end trained ML systems; if anything, that's an indictment of Tesla's approach, not Google's more traditional sensor-based-approach.
