I understand what you are saying, but we may be thinking too small here. Think back to when someone first proposed that a computer could learn a game by simply knowing the basic rules and just making guided random moves to see what worked best. That was so radically different than previous approaches that involved explicitly programming the machine with the very best of human strategies....
In order to teach self driving like we taught AlphaGo, we need a driving simulator that is phenomenal, one that a human could hardly tell is a simulation, down to the variety of cars, people, hedges, weather, etc. This sounds like just as hard a problem as building the thing that can drive in this environment
It seems impossibly difficult to us now to build a sufficiently realistic driving simulator that a computer could learn from because it just seems to complex, to capture all the nuances or the driving game, right? Well maybe there is a better way to look at it that doesn't depend on a detailed model?
This sounds a lot like the early naysayers regarding computer chess ever beating a real human expert saying it would require a programming approach that looked ahead exhaustively at every move and every conceivable outcome of the board, and this would require nearly infinite computational power.
That was small thinking. We had to go outside the box and look at the problem differently.