There's a third type as well - 'Shadow Mode' where the software is running constantly but the driver is in full control.

So if there's an accident, Tesla can check to see if the autopilot would/could have avoided it. If they can turn round to lawmakers and say that "X% of accidents could be avoided if hands-off autopilot was legal" it should help speed up the regulatory side of things.

Yes, but it has to understand the difference between minor human errors (allowing more drift than autopilot) and actual important differences (braking ahead of a potential collision that the autopilot can't see).

So.. like linear regression, and removing outliers to get a more correct fit?

I don't know if it's that easy. There are tons of images and text laying around, but research is focused on just a few datasets. Sometimes it's hard to make use of 100x more data.

I would assume that Google Maps vans aren't driven in the same manner as other cars. They've got a large complicated camera system on top, so presumably they're driven very conservatively, and presumably they're also not driven particularly fast (kind of hard to capture good pictures when you're driving really fast).

Which is to say, if Google wants self-driving Google Maps vans, then collecting data using Google Maps vans makes sense. But if they want general-purpose self-driving cars, then collecting data using Google Maps vans will only give them a very narrow set of data.