Obviously, my message was slightly provocative, deep learning methods and classical controls (which by the way are able to quantify robustness to plant uncertainties and noisy signals) are all very useful but shall be used in combination. End-to-end techniques that bundle perception, planning and control in an opaque net are fun to play with (like in this article), it just very sad to see people believing this produces robust and safety-critical systems and we see too much of such articles on HN.
I agree with you in the sense that if a known and reliable way to map knowledge and information from one domain to another (e.g. from desired trajectory + perceived current position to steering inputs), I'd much prefer that than black box ish neural nets. Neural nets aren't meant to be the silver bullet.
But in this case though, any kind of state space control also requires rather precise knowledge of the physical laws that govern the dynamics of the vehicles. When such information is not available, can neural nets do a decent job at mimicking an analytical control algorithm? I think that's an interesting problem worth exploring.
