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by gugagore 3439 days ago
I agree with you. If you can leverage control theory from the 1950s to solve your problem, what's the point?

However, I will state that using e.g. Lyapunov functions to prove the stability of the system requires a model of the system. And even if you need a guarantee for your system, that guarantee is only as good as the fidelity of your model. For an inexpensive RC car, with slippage and saturation, without torque control or inertial sensing, you're going to have a hard time doing something that sounds as principled as what you suggest.

1 comments

You seem to be forgetting the entire vision pipeline that automatically extracts "lanes" and that information gets incorporated in an end to end manner requiring only true steering angles and nothing else. Its easy to comment but its not as straightforward or trivial as one might assume.
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.