2 ms·

That is a long explanation, but generally even human binocular disparity incorrectly guesses 3D structures and distances. Our brains automatically fill in a lot

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

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