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.

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.

Rather than demeaning someone's effort, learn why specific methods are considered appropriate/state-of-the-art when solving certain problems.

This is an unbelievably wrong comment. All the Lyapunov and traditional Process control theory in the world won't help you solve autonomous driving. Also regarding "Guarantees and Safety" they don't magically appear out of thin air when you use traditional process control especially in noisy domains like autonomous driving. This comment is equivalent of "I can write code to solve Atari Pong in any programming language deterministicly so any post showing Deep Reinforcement Learning is stupid"...

So control theory is ok for unmanned aerial drones but autonomous driving is just too far? Control theory can't handle noisy domains?

Guarantees of safety (more accurately stability) is the entire point of lyaponov analysis, and it's used on noisy systems all of the time (https://www.mathematik.hu-berlin.de/~imkeller/research/paper...). Can you point to a specific noisy system that control theory is ill suited for?

Once you have a path to follow, classical control theory can be used to control the steering angle to follow it.

But classical control theory hasn't been able to extract, from camera pixels, the open path in a road with cars, bicycles, and pedestrians. Camera inputs are million-dimensional, and there aren't accurate theoretical models for them.

Unmanned drones are orders of magnitude easier since you don't have anything that you can just fly into once you are above few hundred feets. They also don't have to rely on any vision based sensing. E.g. a drone has altitude, current speed, heading all of which while noisy can be represented easily as a small set of values.

The whole Lyapunov and control theory assumes perfect knowledge of sensors. Even though the signal itself might be error prone you have a signal. In case of autonomous driving even in simple cases as those described in the blogposts knowing the exact position of the markers and then using them to tune the contoller is not as easy as you might think.

The end-to-end system shown here solves three problems it processes the images to derive the signal, it then represents it optimally to the controller and then tunes the controller using provided training labels.

I cited Lyapunov, more as the ABC of nonlinear controls. Much more can be done in an analytical fashion, the "end-to-end" system here does not "solve" anything. It is a trained steering command regressor, nothing fancy, it's likely to work in this guy's living room, under certain lighting conditions, there is no way of predicting its accuracy, sensibility or anything else. Engineers have been breaking down systems into sub systems for a reason -> tractability of testing and improvement. End-to-end systems like that have close to zero value if you need something reliable.

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.

Why learn to walk when crawling is effective? When crawling you have hard guarantees that you won't fall down.

When falling down actually corresponds to killing a pedestrian, then I'd rather try to understand the complexity of robust walking rather than observing a bunch of humans, mimic their behavior and hope for the best.

> Given a list of time-dependent coordinates of obstacles

A tree falls in an intersection because some carpenter ants chewed through trunk. Cars swerve to miss the tree and collide in an inelastic ball of nonlinearity, showering debris everywhere. You approach this at 65 mph and have 23 ft to decide what to do. Fear not, you have a list, a perfect list with coordinates, velocities, and material properties of every solid body in the area. Furthermore, without great intellectual effort, you can solve the millions of coupled differential equations that govern the dynamics of the entire system in near real time. Oh, and your list also has a measure of importance of each bit of mass, whether it is human, animal, or inert. And your list also accounts for the degrees of freedom introduced by every other car approaching the intersection, also using their own respective lists and perfect knowledge of the world to miss each other?

Do you happen to have a PhD in Robitics?

I think Rockets are straightforward too just a bottle with expanding gasse through a series of nozzles, pointed at different angles at correct time but since I know I am not a rocket scientist I dont go around claiming moon-landing was not a "great intellectual" effort.