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Yes, Teslas manufactured since mid-October all have the hardware for full autonomy[0]. It will be interesting to see how long it takes and what technical and le

Yes, Teslas manufactured since mid-October all have the hardware for full autonomy[0]. It will be interesting to see how long it takes and what technical and legal hurdles are involved. [0] https://www.tesla.com/blog/all-tesla-cars-being-produced-now-have-full-self-driving-hardware https://www.tesla.com/blog/all-tesla-cars-being-produced-now...

deleted 10y ago
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Animats 10y ago
Maybe, maybe not. Tesla still doesn't have LIDAR. Their sensor suite is still much weaker than Google's or Volvo's or Otto's.
- toomuchtodo 10y ago
  Didn't the DARPA grand challenge show self driving was a software problem, not a hardware problem? Tesla has a level 4 autonomy demonstration video on their site. Maybe computer vision is superior to LIDAR; not a lot of human drivers with laser arrays on them driving around.
  - sbierwagen 10y ago
    Animats would indeed be a good person to talk to about CV/LIDAR in the DARPA challenge, since he lead a team that entered it.
    - deleted 10y ago
      [deleted]
  - justinpombrio 10y ago
    > Didn't the DARPA grand challenge show self driving was a software problem, not a hardware problem? Yes, the software is the hard part. That's why it makes sense to use hardware that makes the job of the software easier. LIDAR tells you how far away objects are; if you're using cameras you have to do that with software instead. AFAIK no one yet knows how to do computer vision with cameras in the sorts of situations a self-driving car needs (though I'm not a vision researcher). That means that Tesla is betting on solving a research problem to get the cars they've already sold to start driving themselves. I always thought that was an insanely audacious business plan.
    - jon-wood 10y ago
      To be fair Elon Musk has a track record of coming up with insanely audacious business plans and then following through.
  - wyldfire 10y ago
    I think the safety standard for autonomous car drivers will be much, much higher than human drivers. So it's not fair to compare with just a stereographic camera pair. IMO the most successful implementations are ones that can predict accidents that humans couldn't even see taking place (obscured by other cars, etc).
    - toomuchtodo 10y ago
      > IMO the most successful implementations are ones that can predict accidents that humans couldn't even see taking place (obscured by other cars, etc). Such as Tesla'a forward facing radar, highlighted the other day on HN preventing an additional accident participant by detecting a vehicle slowing down ahead that the driver could not visually detect?
  - robotresearcher 10y ago
    Here's the winning car from the last GC: http://www.tartanracing.org http://www.tartanracing.org Lots of LIDARs, lots of cameras.
    - toomuchtodo 10y ago
      "Stanley", the VW Touareg that won the 2005 grand challenge, also used LIDAR (but fused with camera imagery). In the event that the fused sensor data reduced confidence, the vehicle would slow and lean more heavily on the LIDAR sensors. "Additional guidance data was provided by a video camera used to observe driving conditions out to eighty meters (beyond the range of the LIDAR) and to ensure room enough for acceleration." "Stanley was characterized by a machine learning based approach to obstacle detection. Data from the LIDARs was fused with images from the vision system to perform more distant look-ahead. If a path of drivable terrain could not be detected for at least 40 meters in front of the vehicle, speed was decreased and the LIDARs were used to locate a safe passage." This computer vision processing was performed with 1.6ghz Pentium M processors 11 years ago. Is it so far fetched Nvidia parallel processing hardware can't do better with CCDs than LIDAR?
      - argonaut 10y ago
        This is a research problem, not a compute problem. Directly trying to replicate what LIDAR gives you (SLAM, a 3d map of your current environment) using just cameras is still unsolved today. And it is an active area of research.
        snovv_crash 10y ago
        No, it is solved and there is commercial software that will do this for you. Look at Pix4D, AgiSoft, DroneDeploy, etc. The problem is that it typically doesn't run in real-time, and scales badly with number of images.
        robotresearcher 10y ago
        So it's not a solved problem then. Apart from realtime there is a robustness problem that the offline reconstruction systems don't suffer from too badly. Missing data and poor reconstructions are inconvenient for an offline system but could be deadly in a car.
  - taneq 10y ago
    > a software problem, not a hardware problem I don't think this term even means anything. The problem can be solved by various mixtures of hardware plus software to varying degrees of adequacy. On the one hand, we know that it's possible to drive a vehicle with high reliability in a wide range of circumstances with just two relatively low resolution cameras... if those cameras are feeding into a trained human-level intellect. What combination of hardware and software is required to generate that intellect is still unknown. On the other hand, the problem seems relatively well solved using 3D laser scanners and relatively straightforward software. It works pretty well and just needs to be shaken down in all of the possible circumstances it might encounter. It's also unreasonably expensive. We seem to be converging on an "easiest" solution which uses a mixture of cameras, radars, ultrasound and maybe LIDAR, using deep learning for image processing. That's just one solution, though.
    - halomru 10y ago
      >with just two relatively low resolution cameras... We probably can, but most of us drive with two cameras with approximately 120 megapixels of feature resolution with highly optimized non uniform pixel density and incredible dynamic range. Micromovements allow the software to boost the effective resolution by about a factor of four, to over 400 megapixels. Image stabilization works in multiple layers and executes stabilization movements within 10ms in the fastest layer. The human eyes and vision system are certainly one of our more impressive parts, and we have a really hard time replicating their performance.
  - datguacdoh 10y ago
    That demonstration video is very deceiving. There are many times where they switch camera angles and the car has driven some distance that isn't shown. They used clever editing to make it feel like it was 100% autopilot but if you know the area (I live nearby) you can see the jump cuts. I'm hopeful they can do full autonomy with their sensor package but I'm still skeptical at this point.
- Reason077 10y ago
  Tesla still doesn't have LIDAR. It's not clear that LIDAR offers any real advantages over a camera suite combined with RADAR. LIDAR has the same limitations as optical cameras with regards to rain, fog, etc. As image processing capabilities continue to improve, the case for LIDAR gets weaker.
  - Animats 10y ago
    LIDAR has the same limitations as optical cameras with regards to rain, fog, etc. Actually, no. With appropriate signal processing, LIDAR units can see through rain and fog. The early noise returns from rain and fog can be gated out until the system gets a reflection from something solid. There are expensive LIDAR binoculars that can see through fog. You adjust the minimum range until you're seeing past the fog. So far, automotive LIDAR units don't have that feature, but they could.

deleted 10y ago

[deleted]

Animats 10y ago

Maybe, maybe not. Tesla still doesn't have LIDAR. Their sensor suite is still much weaker than Google's or Volvo's or Otto's.

toomuchtodo 10y ago
Didn't the DARPA grand challenge show self driving was a software problem, not a hardware problem? Tesla has a level 4 autonomy demonstration video on their site. Maybe computer vision is superior to LIDAR; not a lot of human drivers with laser arrays on them driving around.
- sbierwagen 10y ago
  Animats would indeed be a good person to talk to about CV/LIDAR in the DARPA challenge, since he lead a team that entered it.
  - deleted 10y ago
    [deleted]
- justinpombrio 10y ago
  > Didn't the DARPA grand challenge show self driving was a software problem, not a hardware problem? Yes, the software is the hard part. That's why it makes sense to use hardware that makes the job of the software easier. LIDAR tells you how far away objects are; if you're using cameras you have to do that with software instead. AFAIK no one yet knows how to do computer vision with cameras in the sorts of situations a self-driving car needs (though I'm not a vision researcher). That means that Tesla is betting on solving a research problem to get the cars they've already sold to start driving themselves. I always thought that was an insanely audacious business plan.
  - jon-wood 10y ago
    To be fair Elon Musk has a track record of coming up with insanely audacious business plans and then following through.
- wyldfire 10y ago
  I think the safety standard for autonomous car drivers will be much, much higher than human drivers. So it's not fair to compare with just a stereographic camera pair. IMO the most successful implementations are ones that can predict accidents that humans couldn't even see taking place (obscured by other cars, etc).
  - toomuchtodo 10y ago
    > IMO the most successful implementations are ones that can predict accidents that humans couldn't even see taking place (obscured by other cars, etc). Such as Tesla'a forward facing radar, highlighted the other day on HN preventing an additional accident participant by detecting a vehicle slowing down ahead that the driver could not visually detect?
- robotresearcher 10y ago
  Here's the winning car from the last GC: http://www.tartanracing.org http://www.tartanracing.org Lots of LIDARs, lots of cameras.
  - toomuchtodo 10y ago
    "Stanley", the VW Touareg that won the 2005 grand challenge, also used LIDAR (but fused with camera imagery). In the event that the fused sensor data reduced confidence, the vehicle would slow and lean more heavily on the LIDAR sensors. "Additional guidance data was provided by a video camera used to observe driving conditions out to eighty meters (beyond the range of the LIDAR) and to ensure room enough for acceleration." "Stanley was characterized by a machine learning based approach to obstacle detection. Data from the LIDARs was fused with images from the vision system to perform more distant look-ahead. If a path of drivable terrain could not be detected for at least 40 meters in front of the vehicle, speed was decreased and the LIDARs were used to locate a safe passage." This computer vision processing was performed with 1.6ghz Pentium M processors 11 years ago. Is it so far fetched Nvidia parallel processing hardware can't do better with CCDs than LIDAR?
    - argonaut 10y ago
      This is a research problem, not a compute problem. Directly trying to replicate what LIDAR gives you (SLAM, a 3d map of your current environment) using just cameras is still unsolved today. And it is an active area of research.
      - snovv_crash 10y ago
        No, it is solved and there is commercial software that will do this for you. Look at Pix4D, AgiSoft, DroneDeploy, etc. The problem is that it typically doesn't run in real-time, and scales badly with number of images.
        robotresearcher 10y ago
        So it's not a solved problem then. Apart from realtime there is a robustness problem that the offline reconstruction systems don't suffer from too badly. Missing data and poor reconstructions are inconvenient for an offline system but could be deadly in a car.
- taneq 10y ago
  > a software problem, not a hardware problem I don't think this term even means anything. The problem can be solved by various mixtures of hardware plus software to varying degrees of adequacy. On the one hand, we know that it's possible to drive a vehicle with high reliability in a wide range of circumstances with just two relatively low resolution cameras... if those cameras are feeding into a trained human-level intellect. What combination of hardware and software is required to generate that intellect is still unknown. On the other hand, the problem seems relatively well solved using 3D laser scanners and relatively straightforward software. It works pretty well and just needs to be shaken down in all of the possible circumstances it might encounter. It's also unreasonably expensive. We seem to be converging on an "easiest" solution which uses a mixture of cameras, radars, ultrasound and maybe LIDAR, using deep learning for image processing. That's just one solution, though.
  - halomru 10y ago
    >with just two relatively low resolution cameras... We probably can, but most of us drive with two cameras with approximately 120 megapixels of feature resolution with highly optimized non uniform pixel density and incredible dynamic range. Micromovements allow the software to boost the effective resolution by about a factor of four, to over 400 megapixels. Image stabilization works in multiple layers and executes stabilization movements within 10ms in the fastest layer. The human eyes and vision system are certainly one of our more impressive parts, and we have a really hard time replicating their performance.
- datguacdoh 10y ago
  That demonstration video is very deceiving. There are many times where they switch camera angles and the car has driven some distance that isn't shown. They used clever editing to make it feel like it was 100% autopilot but if you know the area (I live nearby) you can see the jump cuts. I'm hopeful they can do full autonomy with their sensor package but I'm still skeptical at this point.
Reason077 10y ago
Tesla still doesn't have LIDAR. It's not clear that LIDAR offers any real advantages over a camera suite combined with RADAR. LIDAR has the same limitations as optical cameras with regards to rain, fog, etc. As image processing capabilities continue to improve, the case for LIDAR gets weaker.
- Animats 10y ago
  LIDAR has the same limitations as optical cameras with regards to rain, fog, etc. Actually, no. With appropriate signal processing, LIDAR units can see through rain and fog. The early noise returns from rain and fog can be gated out until the system gets a reflection from something solid. There are expensive LIDAR binoculars that can see through fog. You adjust the minimum range until you're seeing past the fog. So far, automotive LIDAR units don't have that feature, but they could.