6 ms·

I would definitely want night vision infrared, but my main strategy would be making sure the texture of the road surface is flowing toward me at the expected sp

I would definitely want night vision infrared, but my main strategy would be making sure the texture of the road surface is flowing toward me at the expected speed. I imagine I could use something like the motion vectors in mpeg compression. I would exploit the fact that the road 1000 feet away is coming at me slower than the road 100 feet away: if there is a visual texture in the middle of my field of view (where the horizon should be: road approaching relatively slowly), and it’s coming at me faster than 1000-feet-away asphalt ought to be, I assume the road is obstructed and slow down until I get a clear view again. Granted, I am a lowly web programmer with a hobbyist interest in compression, but I have thought about getting the comma.ai kit and learning how it works, trying to implement my own world modeling, but, sounds like a full time job :) EDIT: as for the headlights, I don’t want to act like I could whip this up in a weekend or anything, but having objects flicker in and out of existence would at least subtract a few points from my confidence score.

dkonofalski 5y ago
That's an interesting way to go about it but would the texture of the road surface be consistent enough and reliable enough to make those calculations? This is the kind of stuff I'm talking about. I definitely do not have the confidence to do that. It's why machine learning is so scary to me. We're basically saying "I can't figure out all the possible permutations of this so let's throw them at a computer and hope it can do a better job".
- jazzyjackson 5y ago
  Can you tell me more about what kind of road (in)consistency you’re describing? It’s fun for me to think of how to improve this strategy. I am similarly opposed/nervous around machine learning, seems impossible to debug weird decisions the black box makes, I would prefer a traditional, deterministic computer vision approach. Basically I want to segment incoming images into a grid and do a cosine similarity score between each cell and its neighbors one frame ago, to detect which direction and how quickly all the textures are moving. So I think it should be robust to whatever the road texture is, provided its not pitch black like wet asphalt.
- avereveard 5y ago
  > consistent enough you want the opposite, if it's consistent, then it's more difficult to gauge it's motion just by finding similar zones. think on mouse on glass or metal, it's too uniform for the sensor to detect motion. anyway: paper https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf lecture: https://www.youtube.com/watch?v=0BVZDyRrYtQ https://www.youtube.com/watch?v=0BVZDyRrYtQ usually circular motion is used, but linear works just as fine

dkonofalski 5y ago

That's an interesting way to go about it but would the texture of the road surface be consistent enough and reliable enough to make those calculations? This is the kind of stuff I'm talking about. I definitely do not have the confidence to do that. It's why machine learning is so scary to me. We're basically saying "I can't figure out all the possible permutations of this so let's throw them at a computer and hope it can do a better job".

jazzyjackson 5y ago
Can you tell me more about what kind of road (in)consistency you’re describing? It’s fun for me to think of how to improve this strategy. I am similarly opposed/nervous around machine learning, seems impossible to debug weird decisions the black box makes, I would prefer a traditional, deterministic computer vision approach. Basically I want to segment incoming images into a grid and do a cosine similarity score between each cell and its neighbors one frame ago, to detect which direction and how quickly all the textures are moving. So I think it should be robust to whatever the road texture is, provided its not pitch black like wet asphalt.
avereveard 5y ago
> consistent enough you want the opposite, if it's consistent, then it's more difficult to gauge it's motion just by finding similar zones. think on mouse on glass or metal, it's too uniform for the sensor to detect motion. anyway: paper https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf https://dspace.mit.edu/bitstream/1721.1/6298/2/AIM-476.pdf lecture: https://www.youtube.com/watch?v=0BVZDyRrYtQ https://www.youtube.com/watch?v=0BVZDyRrYtQ usually circular motion is used, but linear works just as fine