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