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> So in the end I think Elon will be proved right. Those currently investing in LIDAR-based systems will eventually ditch it for purely practical economic reaso

> So in the end I think Elon will be proved right. Those currently investing in LIDAR-based systems will eventually ditch it for purely practical economic reasons. Those that don't will be completely destroyed in the open market. High quality cameras are insanely complex pieces of electronics and optics. Ditto for processors capable of doing quality image recognition. Large scale manufacturing has made them cheap nonetheless. LIDAR is relatively niche, but if it proves useful to deploy it at scale, I'd expect costs to drop very significantly. The underlying technology uses very simple physics (relative to the algorithmic complexity of image recognition); seems like a solid basis to build a sensor off of. > LIDAR is a crutch for bad software You could invert this and say that high precision image recognition is a crutch for ill-suited hardware. The final combination is a product of hardware and software. If LIDAR is currently too expensive or energy-intensive to compete cost-wise at acceptable safety levels, that's one argument, but saying LIDAR is a crutch is just moving the goalposts from "good system" to "cheap hardware". [edit] Also, just want to point out that RADAR resolutions are way too low to operate a vehicle safely (never mind road signage or other things).

BluSyn 7y ago
> The underlying technology uses very simple physics (relative to the algorithmic complexity of image recognition); seems like a solid basis to build a sensor off of. The crux of the argument is you will still need the algorithmic complexity in the end with or without LIDAR, so it doesn't add any advantage. I'll just paste the quote FTA: > "Lidar is really a shortcut," added Tesla AI guru Andrej Karpathy. "It sidesteps the fundamental problems of visual recognition that is necessary for autonomy. It gives a false sense of progress, and is ultimately a crutch."
- stefco_ 7y ago
  > The crux of the argument is you will still need the algorithmic complexity in the end with or without LIDAR, so it doesn't add any advantage. The approach described still uses the same ML on a Lidar data representation, but with an extra step on image recognition to put it into that data format. Image recognition only adds computational complexity post-hardware-sensor because you first need to generate a Lidar-like 3D map. So that is conceivably an advantage for Lidar in the long run given that its method for making the 3D representation of the space (upon which the ML runs) is dirt-simple physics-wise—much simpler than the image recognition algorithms and camera electronics used by image recognition. It's just not cheap yet because the market for processors and cameras is huge and the products have become incredibly sophisticated and cheap. Again, hard to imagine an idea as simple as Lidar not getting much cheaper with scale. > I'll just paste the quote FTA My point wasn't an ad hominem one, so I don't think the fact that an expert like Karpathy said "crutch" changes the fact that it's slanted phrasing. But given that Karpathy works for Tesla and has a vested interest in assuaging investors and consumers, it makes sense why he used slanted phrasing. "Lidar is a crutch" makes Tesla sound more visionary than "we don't want to pay for Lidar because it's still too expensive and we think we'll be able to rival Lidar with image recognition." It's a nice way to subtly jab at competitors who are investing in Lidar and frame it as if Lidar was the tech that had catching up to do (when, in fact, the opposite is true). Since the key point of the article is that ML algorithms work better with Lidar data representations, it's pretty hard to see it any other way. They both go to the same intermediate data representation, and Lidar still wins in a head-to-head. Again, you can argue validly that cameras + image recognition will be good enough, but calling Lidar a "crutch" seems like pro-Tesla spin by a high-up Tesla employee whose job is to make Tesla look good.
- mannykannot 7y ago
  While your comments have gone a long way towards changing my mind, Karpathy's comments are a bit rich, given that Tesla's systems could have benefited from a crutch to help avoid running full-tilt into large obstacles.

BluSyn 7y ago

> The underlying technology uses very simple physics (relative to the algorithmic complexity of image recognition); seems like a solid basis to build a sensor off of. The crux of the argument is you will still need the algorithmic complexity in the end with or without LIDAR, so it doesn't add any advantage. I'll just paste the quote FTA: > "Lidar is really a shortcut," added Tesla AI guru Andrej Karpathy. "It sidesteps the fundamental problems of visual recognition that is necessary for autonomy. It gives a false sense of progress, and is ultimately a crutch."

stefco_ 7y ago
> The crux of the argument is you will still need the algorithmic complexity in the end with or without LIDAR, so it doesn't add any advantage. The approach described still uses the same ML on a Lidar data representation, but with an extra step on image recognition to put it into that data format. Image recognition only adds computational complexity post-hardware-sensor because you first need to generate a Lidar-like 3D map. So that is conceivably an advantage for Lidar in the long run given that its method for making the 3D representation of the space (upon which the ML runs) is dirt-simple physics-wise—much simpler than the image recognition algorithms and camera electronics used by image recognition. It's just not cheap yet because the market for processors and cameras is huge and the products have become incredibly sophisticated and cheap. Again, hard to imagine an idea as simple as Lidar not getting much cheaper with scale. > I'll just paste the quote FTA My point wasn't an ad hominem one, so I don't think the fact that an expert like Karpathy said "crutch" changes the fact that it's slanted phrasing. But given that Karpathy works for Tesla and has a vested interest in assuaging investors and consumers, it makes sense why he used slanted phrasing. "Lidar is a crutch" makes Tesla sound more visionary than "we don't want to pay for Lidar because it's still too expensive and we think we'll be able to rival Lidar with image recognition." It's a nice way to subtly jab at competitors who are investing in Lidar and frame it as if Lidar was the tech that had catching up to do (when, in fact, the opposite is true). Since the key point of the article is that ML algorithms work better with Lidar data representations, it's pretty hard to see it any other way. They both go to the same intermediate data representation, and Lidar still wins in a head-to-head. Again, you can argue validly that cameras + image recognition will be good enough, but calling Lidar a "crutch" seems like pro-Tesla spin by a high-up Tesla employee whose job is to make Tesla look good.
mannykannot 7y ago
While your comments have gone a long way towards changing my mind, Karpathy's comments are a bit rich, given that Tesla's systems could have benefited from a crutch to help avoid running full-tilt into large obstacles.