The big difference is that Google's car are actually self-driving themselves. Tesla's cars will collect a lot of data, but about human driving. The actual automated driving software will have to be tested separately.
1. When the driver has full control of the vehicle
2. When the 'autopilot' is engaged and the driver is ready to intervene if necessary
So if the AI passes the safety test on Type 1 data, Tesla can promote it to being tested on Type 2. And if it passes that safety test it can be promoted to full autonomous control.
The 'autopilot' mode effectively does for Tesla what Google's test drivers do, but for free and on a much larger scale. Seems to me Tesla have a very strong hand here.
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.
This is why "partial automation" for the initial data collection still produces valid results. You can replay data against updated models and do "what if" testing without actually sending the car back out on the road again.
"It would have avoided this accident" (by braking, steering). It can say nothing about the future, "... but it would still have been in a collision 0.42 seconds later".
For all the collisions it would have avoided, there's another subset where it would only have "delayed" the collision. But that won't be mentioned. Because it doesn't fit the narrative.
Eventually, (and with a lot of handwaving) the difference converges towards 0 and the network gives the right answer.
In this case, what you need is mostly "what would most humans do?"
There would be things to refine about that (e.g. prevent speeding; analyse how humans reacted right before crashes etc. and improve on responses), but as a starting point it is immensely useful.