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Very impressive that AP disengaged at the beginning. I don’t have much experience in SDCs, but did some computer vision/ robotics work previously. I truly thi

Very impressive that AP disengaged at the beginning. I don’t have much experience in SDCs, but did some computer vision/ robotics work previously. I truly think that Tesla’s approach, if it works, will win in the long run (ie - next 15-20 years, I think the breakthrough for true L5 isn’t here yet - but will be in improvements in one-shot learning over the next decade). Winter driving is a completely different beast than driving out in Arizona. You have to account for snow banks, slipperiness, lidar obstruction, and control itself - which is much harder. How many thousands of miles of snow driving data does Tesla possess, compared to the #2 player? Tesla’s approach of vision based, AP disengagement-led training will scale better, precisely due to what you’re looking at in this article - yeah, this is an unimpressive demo - but winter driving, rainy driving, dirt road driving are all general cases for Tesla, while they’re special cases for other companies (from what’s been revealed; Comma might be an exception). Most people drive in the place they live, and drive on the highway to visit their friends and parents. It’s easy to be deceived into thinking driving is a small problem space of stop signs and lane changes. But even within just North America, there is enormous variety. There is massive risk involved - these folks are pushing NNs further than any application I’ve seen, but with the last few releases, it suddenly doesn’t look all that crazy. Secondly, combining rules based systems and neural networks is difficult. You end up with the N * M problem, and inevitably end up missing cases. It makes sense to do the absolute basic safety features as rules, and Tesla might be doing this, but all it takes for Tesla’s NN approach to pay off is one leap in explainability. There is an entire industry working on this - and Tesla will be the single biggest benefactor. I don’t know who will win L5 - but I’m putting my bets on Tesla for L4.

new_realist 6y ago
Hasn’t Waymo already won? They’re driverless now.
- stoddur 6y ago
  No, they still use following cars and remote assistance
  - RivieraKid 6y ago
    But they're driverless. This means a very high confidence in the system. Hopefully, they'll reach total confidence next year and get rid of chase cars.
toast0 6y ago
> How many thousands of miles of snow driving data does Tesla possess, compared to the #2 player? It depends. If they're not recording the right things, they have the same 0 bytes of data as everyone else. There's a not insignificant chance that their sensor data is insufficient to perform the tasks, and the recorded data won't be usable for training a system with whatever sensor is needed; in which case, no advantage for logging customer movements for N years.
- judge2020 6y ago
  Yep, Tesla is placing all their eggs into one basket with purely visual self-driving.
  - jjoonathan 6y ago
    Visual-only also delivers less fuel for lawyers to armchair quarterback, and that advantage applies even in the limit of good AI.
  - fiddlerwoaroof 6y ago
    Man, those humans are so dumb thinking they can drive safely just by looking at the road /s
    - toast0 6y ago
      Let me know when Tesla's cameras can put their arms up to shield their eyes from glare. Or move over a few inches to see around a big truck. Or react to a stopped big red fire truck with flashing lights. /s
    - RivieraKid 6y ago
      Why does Tesla bother with radar and ultrasonic sensors? Humans can drive without them.
      - deleted 6y ago
        [deleted]
kajecounterhack 6y ago
> Tesla’s approach of vision based, AP disengagement-led training will scale better ... [about weather] they’re special cases for other companies (from what’s been revealed; Comma might be an exception). > all it takes for Tesla’s NN approach to pay off is one leap in explainability. It's not vision and disengagement-led training that's conferring the scale advantages you're talking about, because other AV companies actually do train on and attempt to drive in various kinds of weather, and safety drivers do the same thing normal drivers do except that they're more trained. The difference is the approach Tesla has to safety (vs the others). Which is to say: Tesla doesn't worry about training their supervising drivers, at the risk of those folks' lives. Tesla also doesn't seem to worry about _proving_ the safety of their systems: explainability isn't simply going to be a matter of a new algorithmic breakthrough. How do you prove that your software is safe if you don't sample heavily from different operating domains you want your car to work in, and then either simulate on those miles or actually test your car? Before letting a driver behind the wheel of an autopilot, companies like Cruise/Waymo/Aurora all do manual driving first in a new domain, and then move to safety drivers (even 2 to start, for extra safety). This is not because their software is incapable of doing the same things as Tesla, but because it's very worthwhile to sample disengage rates and assess safety criticality to various issues before allowing the car to act autonomously. And even then there are safety nets in place to further mitigate risk. (You're probably right that L5 is a long ways off, but this is how we'll get to L4.) Of course it's more "scalable" to avoid all this and put the responsibility of risk on users who, as your system improves, become less and less attentive. And so this is what Tesla does. I suspect solving the long tail of issues is going to be more of a challenge for Tesla because at least in my experience it's easier to {convert a naive rules/simple model --> complex model} than to {debug the complex model --> handle failure modes with logic}.
- nirushiv 6y ago
  Very valid points. I think the key is that you’re going to need a complex model for full self driving. No way around that - even if Waymo starts with a rule based model, they’re going to have to work with black boxes and debug them at some point (and they do). Tesla just decided to jump into the deep end from the beginning.
RivieraKid 6y ago
Fleet data of Tesla's scale probably doesn't give you a substantial advantage. MobileEye seems to be ahead of Tesla with a lidar-less system without such data (but they have partnerships with car makers to collect mapping data). From what I hear, companies like Waymo don't want more data, the bottlenecks are engineering. In self-driving, you basically have 3 parts - perception, prediction and planning. Perception is the easy part (if you have lidars). Tesla and AFAIK others rely heavily on NNs for perception. The real challenge is prediction and planning. To what extent are NNs involved here in Tesla's system is unknown, probably not that much. Overall, I don't think that Tesla has a fundamentally different approach than others.

new_realist 6y ago

Hasn’t Waymo already won? They’re driverless now.

stoddur 6y ago
No, they still use following cars and remote assistance
- RivieraKid 6y ago
  But they're driverless. This means a very high confidence in the system. Hopefully, they'll reach total confidence next year and get rid of chase cars.

toast0 6y ago

> How many thousands of miles of snow driving data does Tesla possess, compared to the #2 player? It depends. If they're not recording the right things, they have the same 0 bytes of data as everyone else. There's a not insignificant chance that their sensor data is insufficient to perform the tasks, and the recorded data won't be usable for training a system with whatever sensor is needed; in which case, no advantage for logging customer movements for N years.

judge2020 6y ago
Yep, Tesla is placing all their eggs into one basket with purely visual self-driving.
- jjoonathan 6y ago
  Visual-only also delivers less fuel for lawyers to armchair quarterback, and that advantage applies even in the limit of good AI.
- fiddlerwoaroof 6y ago
  Man, those humans are so dumb thinking they can drive safely just by looking at the road /s
  - toast0 6y ago
    Let me know when Tesla's cameras can put their arms up to shield their eyes from glare. Or move over a few inches to see around a big truck. Or react to a stopped big red fire truck with flashing lights. /s
  - RivieraKid 6y ago
    Why does Tesla bother with radar and ultrasonic sensors? Humans can drive without them.
    - deleted 6y ago
      [deleted]

kajecounterhack 6y ago

> Tesla’s approach of vision based, AP disengagement-led training will scale better ... [about weather] they’re special cases for other companies (from what’s been revealed; Comma might be an exception). > all it takes for Tesla’s NN approach to pay off is one leap in explainability. It's not vision and disengagement-led training that's conferring the scale advantages you're talking about, because other AV companies actually do train on and attempt to drive in various kinds of weather, and safety drivers do the same thing normal drivers do except that they're more trained. The difference is the approach Tesla has to safety (vs the others). Which is to say: Tesla doesn't worry about training their supervising drivers, at the risk of those folks' lives. Tesla also doesn't seem to worry about _proving_ the safety of their systems: explainability isn't simply going to be a matter of a new algorithmic breakthrough. How do you prove that your software is safe if you don't sample heavily from different operating domains you want your car to work in, and then either simulate on those miles or actually test your car? Before letting a driver behind the wheel of an autopilot, companies like Cruise/Waymo/Aurora all do manual driving first in a new domain, and then move to safety drivers (even 2 to start, for extra safety). This is not because their software is incapable of doing the same things as Tesla, but because it's very worthwhile to sample disengage rates and assess safety criticality to various issues before allowing the car to act autonomously. And even then there are safety nets in place to further mitigate risk. (You're probably right that L5 is a long ways off, but this is how we'll get to L4.) Of course it's more "scalable" to avoid all this and put the responsibility of risk on users who, as your system improves, become less and less attentive. And so this is what Tesla does. I suspect solving the long tail of issues is going to be more of a challenge for Tesla because at least in my experience it's easier to {convert a naive rules/simple model --> complex model} than to {debug the complex model --> handle failure modes with logic}.

nirushiv 6y ago
Very valid points. I think the key is that you’re going to need a complex model for full self driving. No way around that - even if Waymo starts with a rule based model, they’re going to have to work with black boxes and debug them at some point (and they do). Tesla just decided to jump into the deep end from the beginning.

RivieraKid 6y ago

Fleet data of Tesla's scale probably doesn't give you a substantial advantage. MobileEye seems to be ahead of Tesla with a lidar-less system without such data (but they have partnerships with car makers to collect mapping data). From what I hear, companies like Waymo don't want more data, the bottlenecks are engineering. In self-driving, you basically have 3 parts - perception, prediction and planning. Perception is the easy part (if you have lidars). Tesla and AFAIK others rely heavily on NNs for perception. The real challenge is prediction and planning. To what extent are NNs involved here in Tesla's system is unknown, probably not that much. Overall, I don't think that Tesla has a fundamentally different approach than others.