The thing is you already know everything you need to know about all 4 billion floats. Collecting data on every intersection in the US is quite difficult.

Tesla does however collect data on edge cases and then train their system to respond correctly. They can for example trail a collection network to identify things that might be obscured stop signs, then have the fleet collect a whole bunch of examples, hand label those samples, and roll this new data in to the training system. This is explicitly how they handle edge cases.

They can also create a new feature or network and roll it out in “shadow mode” where it is running but has no influence on the car, and then they can observe how these systems are behaving in the real world.

The real issue I guess is when they release a new feature without trialing it in shadow mode, or if they have gaps in their testing and validation system.