5 ms·

Obviously, "Lane Following Autopilot using my brain and controls theory" would not make it to the top of HN. Welcome to the new era where Tensorflow replaces Ly

by webaba 10y ago

Obviously, "Lane Following Autopilot using my brain and controls theory" would not make it to the top of HN. Welcome to the new era where Tensorflow replaces Lyapunov and ML spares you the need of understanding hard problems... until you need guarantees and safety... but but it's ok let's add more data.

deleted 10y ago
[deleted]
gugagore 10y 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.
- deleted 10y ago
  [deleted]
- aub3bhat 10y ago
  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.
  - gugagore 10y ago
    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.
    - deleted 10y ago
      [deleted]
  - gugagore 10y ago
    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.
  - webaba 10y ago
    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.
    - ogrisel 10y ago
      But to be able to decouple the vision pipeline you need a lot of manual annotation work which is tedious.
      - argonaut 10y ago
        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.
yessql 10y ago
Why learn to walk when crawling is effective? When crawling you have hard guarantees that you won't fall down.
- webaba 10y ago
  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.
amelius 10y ago
Also, general autonomous driving isn't as hard as it sounds. Given a list of time-dependent coordinates of obstacles, it should be pretty easy to navigate around such that no collision occurs. The hardest part is testing, but this is just a matter of tedious work and doesn't require great intellectual effort.
- aub3bhat 10y ago
  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.
- willis77 10y ago
  > 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?
aub3bhat 10y ago
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"...
- quinnchr 10y ago
  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/papers/jsv_kramer_new.pdf https://www.mathematik.hu-berlin.de/~imkeller/research/paper...). Can you point to a specific noisy system that control theory is ill suited for?
  - aub3bhat 10y ago
    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.
    - webaba 10y ago
deleted 10y ago
[deleted]

by webaba 10y ago

deleted 10y ago

[deleted]

gugagore 10y 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.

deleted 10y ago
[deleted]
aub3bhat 10y ago
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.
- gugagore 10y ago
  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.
  - deleted 10y ago
    [deleted]
- gugagore 10y ago
  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.
- webaba 10y ago
  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.
  - ogrisel 10y ago
    But to be able to decouple the vision pipeline you need a lot of manual annotation work which is tedious.
    - argonaut 10y ago
      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.

yessql 10y ago

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

webaba 10y ago
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.

amelius 10y ago

Also, general autonomous driving isn't as hard as it sounds. Given a list of time-dependent coordinates of obstacles, it should be pretty easy to navigate around such that no collision occurs. The hardest part is testing, but this is just a matter of tedious work and doesn't require great intellectual effort.

aub3bhat 10y ago
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.
willis77 10y ago
> 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?

aub3bhat 10y ago

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"...

quinnchr 10y ago
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/papers/jsv_kramer_new.pdf https://www.mathematik.hu-berlin.de/~imkeller/research/paper...). Can you point to a specific noisy system that control theory is ill suited for?
- aub3bhat 10y ago
  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.
  - webaba 10y ago

deleted 10y ago

[deleted]