5 ms·

Seems odd to me that the autopilot doesn't use them together, as it would seem that the autopilot would easily be able to see with the camera whether it was a s

by zarriak 10y ago

Seems odd to me that the autopilot doesn't use them together, as it would seem that the autopilot would easily be able to see with the camera whether it was a sign, as at least in the United States they seem a somewhat uniform green.

daveguy 10y ago
I think the likely answer is Tesla "autopilot" is a glorified cruise control + lane assist + data collection for future intelligent controls. Google probably has close to this capability, but they are at the leading edge of image recognition. In order to depend on it the AI would have to be able to distinguish 99.9+% and would have to be able to tell the difference between a green truck and a road sign. I honestly think self driving cars will require intelligence indistinguishable from artificial general intelligence. This was state of the art in 2014 (improved in 2015, but this paper is on arxiv and gives the idea): http://arxiv.org/pdf/1409.0575 http://arxiv.org/pdf/1409.0575 Even top scoring Google had classification errors over 6% of the time in 2014. Object localization was much worse. Even if it is at 99% that means 1 of every 100 object will be misidentified. You probably identify that many random (non-car, non-sign, etc) objects in a week driving, maybe in a day. That kind of error is unacceptable in self-driving cars and it is state of the art. That is one of the reasons they are still testing and not selling self-driving cars at Google. Better than human may not require quite 99.9+% object classification, but I do wonder how great the self-driving car records would be if someone wasn't always there to take the controls. It certainly wouldn't be "better than human" at this point.
- rasz_pl 10y ago
  Yes, Tesla system is a fancy lane following dressed in powerful PR. Btw I dont feel Imagenet results are relevant, it only tests static 2d images. Car system can have multiple cameras providing 3d reconstruction in near realtime. Mobileye (what tesla repackaged) probably has only one front facing camera and is too stupid beyond 'hey this uniformly colored blob is in my lane'
  - daveguy 10y ago
    I was putting image net performance out there as a bare minimum capability. I agree 3d real-time reconstruction is harder (although aided by multiple cameras + radar, etc). You're right, it probably isn't a fair comparison because of the convolution of real-time needs and additional sensors/cameras available.
    - rasz_pl 10y ago
      I meant the opposite, its immensely easier guessing shapes on a moving platform with multiple cameras providing 3d reconstruction than guessing exact name of an object from one random photo. Car system doesnt need to know the name of a dog breed, all it needs is a shape on a collision course. You can do stereo 3d reconstruction on a 10 year old laptop in real time. You can even do 3d reconstruction with one camera in real time as long as objects move slightly perpendicular to camera http://www.robots.ox.ac.uk/~gk/PTAM/ http://www.robots.ox.ac.uk/~gk/PTAM/ http://vision.in.tum.de/research/vslam/lsdslam http://vision.in.tum.de/research/vslam/lsdslam.
      - daveguy 10y ago
        You definitely need to know more than just "3d blob at position x" because your expectations and avoidance will be different for car, truck, motorcycle, road sign, human and especially for things like "grocery sack" vs "cinder block". Less relevant on the hwy, but it definitely isn't simply an "easier" visual task. It is convoluted with things that make it easier (multiple cameras) and things that make it more difficult (real time adaptation). Edit: even not needing to know details like "dog breed" it still needs at least a solid 200 object classes to cover not-so-edge cases (which is what had the 6% error rate in 2014).
        rasz_pl 10y ago
        I dont think so. Walk before you run. There is no difference between a human, bale of hay or a deer when it comes to objects entering your trajectory. Braking in front of all 3d elements obstructing the way is preferable over Tesla decapitation(TM). Real time is not difficult, we got there 10 years ago for primitive 3d reconstruction at 30Hz on ordinary laptop, today its doable on a phone. It would definitely be better than single camera + pattern matching (current mobileye).

by zarriak 10y ago

daveguy 10y ago

I think the likely answer is Tesla "autopilot" is a glorified cruise control + lane assist + data collection for future intelligent controls. Google probably has close to this capability, but they are at the leading edge of image recognition. In order to depend on it the AI would have to be able to distinguish 99.9+% and would have to be able to tell the difference between a green truck and a road sign. I honestly think self driving cars will require intelligence indistinguishable from artificial general intelligence. This was state of the art in 2014 (improved in 2015, but this paper is on arxiv and gives the idea): http://arxiv.org/pdf/1409.0575 http://arxiv.org/pdf/1409.0575 Even top scoring Google had classification errors over 6% of the time in 2014. Object localization was much worse. Even if it is at 99% that means 1 of every 100 object will be misidentified. You probably identify that many random (non-car, non-sign, etc) objects in a week driving, maybe in a day. That kind of error is unacceptable in self-driving cars and it is state of the art. That is one of the reasons they are still testing and not selling self-driving cars at Google. Better than human may not require quite 99.9+% object classification, but I do wonder how great the self-driving car records would be if someone wasn't always there to take the controls. It certainly wouldn't be "better than human" at this point.

rasz_pl 10y ago
Yes, Tesla system is a fancy lane following dressed in powerful PR. Btw I dont feel Imagenet results are relevant, it only tests static 2d images. Car system can have multiple cameras providing 3d reconstruction in near realtime. Mobileye (what tesla repackaged) probably has only one front facing camera and is too stupid beyond 'hey this uniformly colored blob is in my lane'
- daveguy 10y ago
  I was putting image net performance out there as a bare minimum capability. I agree 3d real-time reconstruction is harder (although aided by multiple cameras + radar, etc). You're right, it probably isn't a fair comparison because of the convolution of real-time needs and additional sensors/cameras available.
  - rasz_pl 10y ago
    I meant the opposite, its immensely easier guessing shapes on a moving platform with multiple cameras providing 3d reconstruction than guessing exact name of an object from one random photo. Car system doesnt need to know the name of a dog breed, all it needs is a shape on a collision course. You can do stereo 3d reconstruction on a 10 year old laptop in real time. You can even do 3d reconstruction with one camera in real time as long as objects move slightly perpendicular to camera http://www.robots.ox.ac.uk/~gk/PTAM/ http://www.robots.ox.ac.uk/~gk/PTAM/ http://vision.in.tum.de/research/vslam/lsdslam http://vision.in.tum.de/research/vslam/lsdslam.
    - daveguy 10y ago
      You definitely need to know more than just "3d blob at position x" because your expectations and avoidance will be different for car, truck, motorcycle, road sign, human and especially for things like "grocery sack" vs "cinder block". Less relevant on the hwy, but it definitely isn't simply an "easier" visual task. It is convoluted with things that make it easier (multiple cameras) and things that make it more difficult (real time adaptation). Edit: even not needing to know details like "dog breed" it still needs at least a solid 200 object classes to cover not-so-edge cases (which is what had the 6% error rate in 2014).
      - rasz_pl 10y ago
        I dont think so. Walk before you run. There is no difference between a human, bale of hay or a deer when it comes to objects entering your trajectory. Braking in front of all 3d elements obstructing the way is preferable over Tesla decapitation(TM). Real time is not difficult, we got there 10 years ago for primitive 3d reconstruction at 30Hz on ordinary laptop, today its doable on a phone. It would definitely be better than single camera + pattern matching (current mobileye).