Indeed, I was not really talking about the vision pipeline. But once you decouple the problem (use ML for vision, planning for the trajectory, controls for the rest), you'll get much more stability, guarantees and insight into how to improve your problem. These kinds of end-to-end approaches are very hard to evaluate, they have zero educational value, are not parsimonious and tend to reduce people's analytical skills.

But to be able to decouple the vision pipeline you need a lot of manual annotation work which is tedious.

Tedious, and also solved for a decade already. Also, it's much easier to just find lanes using traditional CV and simply using annotators to verify the lane labels.

You can never use 1950s control theory to solve your problem? I think you didn't understand the my comment, so please let me clarify: I was claiming that even the control problems in this RC-car-lane-keeping domain can benefit from learning approaches.

Are you disagreeing with my comment? Or stating that I should have included additional points in my comment?

In any case I think I understand your comment, that in addition to the control problem, there's a perception problem.