"Keep the car in a straight line" or "turn right, 10°" are the kinds of operation that will always be more performant, take less engineering, and more reliable to create as an old-fashioned simple math function than by using machine learning.

One would expect the output of the neural nets to be encoded in those.

It's the exceptional cases that get you:

1. "Keep the car in a straight line" => The road is closed or there is a detour, or the lane markers aren't visible (or incorrectly marked like on the Palo Alto stretch of the 101 where there are two sets of lane markers; one new (the "actual" markers) and another old but barely faded and still very visible.

2. "Turn right, 10 degree" => With what steering radius? What if the road is bumpy or sloped and the driver has to compensate with the wheel?

etc.

These corner cases are everyday occurrences and lend a necessity for something far more sophisticated than a mere ad hoc model to represent and control human-level driving behaviors with a finite set of rudimentary actions.

Actuator inputs like steering and pedals are almost always represented using continuous values rather than discrete sets for these very reasons.