5 ms·

Camera based guidance systems are unreliable, and the number of edge-case failure modes grow exponential in time. LIDAR/LADAR based systems are not perfect, bu

Camera based guidance systems are unreliable, and the number of edge-case failure modes grow exponential in time. LIDAR/LADAR based systems are not perfect, but do offer mm precision for guidance systems. SLAM based LIDAR systems can be very good, but are also not perfect when forced to guess where a platform is located. Cheers, =3

Bluestein 1y ago
> edge-case failure modes grow exponential in time. How so? Honestly asking.-
- Joel_Mckay 1y ago
  That is a long explanation, but generally even human binocular disparity incorrectly guesses 3D structures and distances. Our brains automatically fill in a lot of missing information that computers just can't know a priori (example: you know a dog hidden behind a car doesn't actually vanish nor remain stationary.) Most guidance platforms would use LIDAR/SLAM to describe the local road surface, and overlap camera vision data to extrapolate distant surfaces and objects. Note distant objects also have lower resolution, unknown non-distinctive features (speed bump, or open man-hole cover etc.), and increasing sparse data as velocity effectively lowers world-state sampling rates. The world-state is constantly changing at every intersection, sampling constraints add latency, and the navigation way-point goals may reach contradiction with immediate path-planning due to ambiguous/expired information. Cheers =3
  - peterfirefly 1y ago
    So, it doesn't "grow exponentially", you just wanted to express that there are "many". > you know a dog hidden behind a car doesn't actually vanish nor remain stationary. Computers can do that, too. It's not that different ... guessing missing words. I think it was two years ago that Tesla told the public that they could do that.
    - Joel_Mckay 1y ago
      >Tesla told the public that they could do that. Lol, really? They either developed superman x-ray vision, or just tracked object occlusion with a common re-acquisition mitigation (so worthless when physical inertia carries a vehicle into an object collision.) >So, it doesn't "grow exponentially", The further the object... the more possible choices will need to be made in the guidance system. Note, guidance and navigation are related, but different problem domains. Roughly, the possible choices (and errors) if I recall grew by: ((m cars) * (k lanes ) * (r occlusions) * (s sign laws) * (1 + world_sate_delta_error(t)) ) ^ (n intersections + w way-points) = 1/hype_correction ...but that doesn't even cover the projected future risk(t). =3
    - Bluestein 1y ago
      I wonder if - at some point - AI might not do the "filling in" of information the brain does by generation (ie. filling in the gaps) or something.-
      - Joel_Mckay 1y ago
        Maybe, but first a real "AI" will need to be invented. I suspect a Amish horse buggy is more practical. =3
- rightbyte 1y ago
  Failures in automotive programs are surely linear to time over a year with all seasons.
  - Joel_Mckay 1y ago
    Failure rates are a different area, and most engineers have proven it is not linear. =3 https://en.wikipedia.org/wiki/Bathtub_curve https://en.wikipedia.org/wiki/Bathtub_curve
    - maxlin 1y ago
      [flagged]
      - Joel_Mckay 1y ago
        They were conflating computational feasibility with sensor device failures. Ask questions if you don't know, as being rude is not constructive. =3
        maxlin 1y ago
        Quite rich for you to accuse others of being rude. My comparison is apt, borne of your own behavior.
        jfyi 1y ago
        He's kind of entertaining. I usually don't do this here and feel it's normally in bad taste, but he crumbles pretty spectacularly when pushed and I was curious how much material he had for me. You should see him ascribing nephrotoxicity to a chemical from a mushroom based on the reactions of squirrels in his yard eating an entirely different mushroom. I think I really got him at his peak though just skimming through.
        Joel_Mckay 1y ago
        Try not to thread hijack, and read the papers you were sent =3 https://en.wikipedia.org/wiki/Autism https://en.wikipedia.org/wiki/Autism
maxlin 1y ago
Human bicameral driven cars are unreliable too, but FSD is more reliable. And more reliable, with way more data than any competing technologies. Using vision for driving is something that has worked for as long as cars have existed. Trying to push some "millimeter precision" solution with unproven feature set and prohibitive hardware accessibility is just asking for no real safety improvements and just more lives lost. Cheers.
- Joel_Mckay 1y ago
  It is true most humans can not track more than 5 objects concurrently. However, lidar data sets tend to produce sparse unambiguous point cloud data that is a lot more feasible to process. For example, a picture/reflection of a road on a bus is not a viable option with 3D data. Cheers =3 https://www.youtube.com/watch?v=vJG698U2Mvo https://www.youtube.com/watch?v=vJG698U2Mvo
  - maxlin 1y ago
    Adding some stat of x things tracked adding top of your stat of "millimeters" really shows the level you're arguing here. Real world doesn't care about exact stats on some simply enumerable attributes, but adaptability. You might see 10 bisons but will instantly react to a scorpion in middle of them in your vision. In any case. Trying to argue against vision even if LIDAR hypothetically was better (it isn't) would just lead to more deaths, maybe at best shielding the rich driving in cities. FSD's stats don't lie :|
    - Joel_Mckay 1y ago
      My point was LIDAR data removes the ambiguity of camera derived data, is not fooled by featureless area ambiguity, and it is computationally easier to handle sparse data. Many high-end multi-beam lidar also embed things like basic Bicycle and Pedestrian object detection in the sensor front end. Things have improved significantly with sensors, but the risk is never 0. The rate of death doesn't really override an expectation of product safety, and humans understanding other humans intent. Have a wonderful day =3

Bluestein 1y ago

> edge-case failure modes grow exponential in time. How so? Honestly asking.-

Joel_Mckay 1y ago
That is a long explanation, but generally even human binocular disparity incorrectly guesses 3D structures and distances. Our brains automatically fill in a lot of missing information that computers just can't know a priori (example: you know a dog hidden behind a car doesn't actually vanish nor remain stationary.) Most guidance platforms would use LIDAR/SLAM to describe the local road surface, and overlap camera vision data to extrapolate distant surfaces and objects. Note distant objects also have lower resolution, unknown non-distinctive features (speed bump, or open man-hole cover etc.), and increasing sparse data as velocity effectively lowers world-state sampling rates. The world-state is constantly changing at every intersection, sampling constraints add latency, and the navigation way-point goals may reach contradiction with immediate path-planning due to ambiguous/expired information. Cheers =3
- peterfirefly 1y ago
  So, it doesn't "grow exponentially", you just wanted to express that there are "many". > you know a dog hidden behind a car doesn't actually vanish nor remain stationary. Computers can do that, too. It's not that different ... guessing missing words. I think it was two years ago that Tesla told the public that they could do that.
  - Joel_Mckay 1y ago
    >Tesla told the public that they could do that. Lol, really? They either developed superman x-ray vision, or just tracked object occlusion with a common re-acquisition mitigation (so worthless when physical inertia carries a vehicle into an object collision.) >So, it doesn't "grow exponentially", The further the object... the more possible choices will need to be made in the guidance system. Note, guidance and navigation are related, but different problem domains. Roughly, the possible choices (and errors) if I recall grew by: ((m cars) * (k lanes ) * (r occlusions) * (s sign laws) * (1 + world_sate_delta_error(t)) ) ^ (n intersections + w way-points) = 1/hype_correction ...but that doesn't even cover the projected future risk(t). =3
  - Bluestein 1y ago
    I wonder if - at some point - AI might not do the "filling in" of information the brain does by generation (ie. filling in the gaps) or something.-
    - Joel_Mckay 1y ago
      Maybe, but first a real "AI" will need to be invented. I suspect a Amish horse buggy is more practical. =3
rightbyte 1y ago
Failures in automotive programs are surely linear to time over a year with all seasons.
- Joel_Mckay 1y ago
  Failure rates are a different area, and most engineers have proven it is not linear. =3 https://en.wikipedia.org/wiki/Bathtub_curve https://en.wikipedia.org/wiki/Bathtub_curve
  - maxlin 1y ago
    [flagged]
    - Joel_Mckay 1y ago
      They were conflating computational feasibility with sensor device failures. Ask questions if you don't know, as being rude is not constructive. =3
      - maxlin 1y ago
        Quite rich for you to accuse others of being rude. My comparison is apt, borne of your own behavior.
        jfyi 1y ago
        He's kind of entertaining. I usually don't do this here and feel it's normally in bad taste, but he crumbles pretty spectacularly when pushed and I was curious how much material he had for me. You should see him ascribing nephrotoxicity to a chemical from a mushroom based on the reactions of squirrels in his yard eating an entirely different mushroom. I think I really got him at his peak though just skimming through.
        Joel_Mckay 1y ago
        Try not to thread hijack, and read the papers you were sent =3 https://en.wikipedia.org/wiki/Autism https://en.wikipedia.org/wiki/Autism

maxlin 1y ago

Human bicameral driven cars are unreliable too, but FSD is more reliable. And more reliable, with way more data than any competing technologies. Using vision for driving is something that has worked for as long as cars have existed. Trying to push some "millimeter precision" solution with unproven feature set and prohibitive hardware accessibility is just asking for no real safety improvements and just more lives lost. Cheers.

Joel_Mckay 1y ago
It is true most humans can not track more than 5 objects concurrently. However, lidar data sets tend to produce sparse unambiguous point cloud data that is a lot more feasible to process. For example, a picture/reflection of a road on a bus is not a viable option with 3D data. Cheers =3 https://www.youtube.com/watch?v=vJG698U2Mvo https://www.youtube.com/watch?v=vJG698U2Mvo
- maxlin 1y ago
  Adding some stat of x things tracked adding top of your stat of "millimeters" really shows the level you're arguing here. Real world doesn't care about exact stats on some simply enumerable attributes, but adaptability. You might see 10 bisons but will instantly react to a scorpion in middle of them in your vision. In any case. Trying to argue against vision even if LIDAR hypothetically was better (it isn't) would just lead to more deaths, maybe at best shielding the rich driving in cities. FSD's stats don't lie :|
  - Joel_Mckay 1y ago
    My point was LIDAR data removes the ambiguity of camera derived data, is not fooled by featureless area ambiguity, and it is computationally easier to handle sparse data. Many high-end multi-beam lidar also embed things like basic Bicycle and Pedestrian object detection in the sensor front end. Things have improved significantly with sensors, but the risk is never 0. The rate of death doesn't really override an expectation of product safety, and humans understanding other humans intent. Have a wonderful day =3