5 ms·

Cost is not the only point he was making. The problem you need to solve is not just “Is there something?”, but also “What is it? And where is it going to move?”

by leobg 5y ago

Cost is not the only point he was making. The problem you need to solve is not just “Is there something?”, but also “What is it? And where is it going to move?”. LIDAR cannot do that. Or at least if you get LIDAR to do that, then you would have also been able to get it done with a camera, in which case you wouldn’t have needed LIDAR in the first place. LIDAR certainly is the low hanging fruit when it comes to the firmer question though (i.e. what is there in my path right now).

deleted 5y ago
[deleted]
2muchcoffeeman 5y ago
But AI and ML isn't as good as a human brain or maybe any brain. I imagine the gap has to be closed with better and multiple sensors or make fundamental leaps in computing technology.
therouwboat 5y ago
LIDAR works in dark and in bad weather, its useful to know there is something in front of you, even if you dont know what it is.
- fastball 5y ago
  What bad weather are you talking about where LiDAR works well? It notably does not perform well (or really at all) in rain/snow/fog. You might be thinking of radar, which Tesla is also no longer putting in their cars.
  - CamperBob2 5y ago
    I've never understood their reasoning. It sounds like a Most Interesting Man in the World commercial: "I don't always tackle the hardest AI problems known to mankind, but when I do, I tie one hand behind my back by not fusing data from every possible sensor I can find in the DigiKey catalog." IR lidar would be pretty useful in rain and fog, I'd think. But I'd rather have all three -- lidar, radar, and visual. Hell, throw in ultrasonic sonar too. That's what Kalman filters are for. Maybe then the system will notice that it's about to ram a fire truck.
    - fastball 5y ago
      The puzzle piece you are missing is that sensor fusion is not an easy problem either. The Tesla perspective is that adding N different sensors into the mix means you now have N*M problems instead of M.
      - CamperBob2 5y ago
        I hope that's not their perspective, because that perspective would be wrong. There are entire subdisciplines of control theory devoted to sensor fusion, and it's not particularly new. Rule 1: More information is better. Rule 2: If the information is unreliable (and what information isn't?), see rule 1. Some potential improvements are relatively trivial, even without getting into the hardcore linear algebra. If the camera doesn't see an obstacle but both radar and lidar do, that's an opportunity to fail relatively safely (potential false braking) rather than failing in a way that causes horrific crashes. Bottom line: if you can't do sensor fusion, you literally have no business working on leading-edge AI/ML applications.
- postmeta 5y ago
  LIDAR does not work in bad weather, fog or heavy snow looks like an oncoming wall.
eightails 5y ago
One question I've always had about Tesla's sensor approach: why not use binocular forward facing vision? Seems like it would be a simple and cheap way to get reliable depth maps, which might help performance in the situations which currently challenge the ML. Detecting whether a stationary object (emergency vehicle or child or whatever) is part of the background would be a lot easier with an accurate depth map, or so it seems to me. Plus using the same cameras would help prevent the issues with sensor fusion of the radar described by Tesla due to the low resolution of the radar. I know the b-pillar cameras exist, but I don't think their FOV covers the entire forward view, and I don't think they have the same resolution as the main forward cameras (partly due to wide FOV). I'd love to hear why I'm wrong though.
- simondotau 5y ago
  > why not use binocular forward facing vision? Because Tesla have demonstrated that it's unnecessary. The depth information they are getting from the forward-facing camera is exceptional. Their vision stack now produces depth information that is dramatically superior to that from a forward-facing radar. https://www.youtube.com/watch?v=g6bOwQdCJrc&t=556s https://www.youtube.com/watch?v=g6bOwQdCJrc&t=556s (It's also worth noting that depth information can be validated when the vehicle is in motion, because a camera in motion has the ability to see the scene from multiple angles, just like a binocular configuration. This is how Tesla trains the neural networks to determine depth from the camera data.)
  - jtc331 5y ago
    Which raises the question of why it was so easy to demonstrate it failing at CES.
    - simondotau 5y ago
      Because the software running in release mode is a much, much older legacy stack. (Do we know if the vehicle being tested was equipped with radar or vision only?)
  - 9935c101ab17a66 5y ago

by leobg 5y ago

deleted 5y ago

[deleted]

2muchcoffeeman 5y ago

But AI and ML isn't as good as a human brain or maybe any brain. I imagine the gap has to be closed with better and multiple sensors or make fundamental leaps in computing technology.

therouwboat 5y ago

LIDAR works in dark and in bad weather, its useful to know there is something in front of you, even if you dont know what it is.

fastball 5y ago
What bad weather are you talking about where LiDAR works well? It notably does not perform well (or really at all) in rain/snow/fog. You might be thinking of radar, which Tesla is also no longer putting in their cars.
- CamperBob2 5y ago
  I've never understood their reasoning. It sounds like a Most Interesting Man in the World commercial: "I don't always tackle the hardest AI problems known to mankind, but when I do, I tie one hand behind my back by not fusing data from every possible sensor I can find in the DigiKey catalog." IR lidar would be pretty useful in rain and fog, I'd think. But I'd rather have all three -- lidar, radar, and visual. Hell, throw in ultrasonic sonar too. That's what Kalman filters are for. Maybe then the system will notice that it's about to ram a fire truck.
  - fastball 5y ago
    The puzzle piece you are missing is that sensor fusion is not an easy problem either. The Tesla perspective is that adding N different sensors into the mix means you now have N*M problems instead of M.
    - CamperBob2 5y ago
      I hope that's not their perspective, because that perspective would be wrong. There are entire subdisciplines of control theory devoted to sensor fusion, and it's not particularly new. Rule 1: More information is better. Rule 2: If the information is unreliable (and what information isn't?), see rule 1. Some potential improvements are relatively trivial, even without getting into the hardcore linear algebra. If the camera doesn't see an obstacle but both radar and lidar do, that's an opportunity to fail relatively safely (potential false braking) rather than failing in a way that causes horrific crashes. Bottom line: if you can't do sensor fusion, you literally have no business working on leading-edge AI/ML applications.
postmeta 5y ago
LIDAR does not work in bad weather, fog or heavy snow looks like an oncoming wall.

eightails 5y ago

One question I've always had about Tesla's sensor approach: why not use binocular forward facing vision? Seems like it would be a simple and cheap way to get reliable depth maps, which might help performance in the situations which currently challenge the ML. Detecting whether a stationary object (emergency vehicle or child or whatever) is part of the background would be a lot easier with an accurate depth map, or so it seems to me. Plus using the same cameras would help prevent the issues with sensor fusion of the radar described by Tesla due to the low resolution of the radar. I know the b-pillar cameras exist, but I don't think their FOV covers the entire forward view, and I don't think they have the same resolution as the main forward cameras (partly due to wide FOV). I'd love to hear why I'm wrong though.

simondotau 5y ago
> why not use binocular forward facing vision? Because Tesla have demonstrated that it's unnecessary. The depth information they are getting from the forward-facing camera is exceptional. Their vision stack now produces depth information that is dramatically superior to that from a forward-facing radar. https://www.youtube.com/watch?v=g6bOwQdCJrc&t=556s https://www.youtube.com/watch?v=g6bOwQdCJrc&t=556s (It's also worth noting that depth information can be validated when the vehicle is in motion, because a camera in motion has the ability to see the scene from multiple angles, just like a binocular configuration. This is how Tesla trains the neural networks to determine depth from the camera data.)
- jtc331 5y ago
  Which raises the question of why it was so easy to demonstrate it failing at CES.
  - simondotau 5y ago
    Because the software running in release mode is a much, much older legacy stack. (Do we know if the vehicle being tested was equipped with radar or vision only?)
- 9935c101ab17a66 5y ago