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Exactly. What is rarely mentioned is his exact quote on the reasoning for radar vs. lidar: “If you’re going to use active photon generation, don’t use visible

by BluSyn 7y ago

Exactly. What is rarely mentioned is his exact quote on the reasoning for radar vs. lidar: “If you’re going to use active photon generation, don’t use visible wavelength, because with passive optical you’ve taken care of all visible wavelength stuff. You want to use a wavelength that’s occlusion-penetrating like radar. LIDAR is just active photon generation in the visible spectrum.” This article is still missing the point when talking about redundancies. LIDAR only works in essentially perfect weather ("not occlusion-penetrating"). Even if it serves only as a "redundancy" there's no advantage in relying on a sensor suite that operates in a less-safe mode in the most adverse road conditions. So basically if you are driving in snow or fog, your LIDAR-based AV has to fall back to Radar+Cameras. If that system can pass all the safety tests in the worst-case road condition then there is no value in the additional sensors that add expense but no safety margin. What's even more overlooked is power consumption. LIDAR is far more power intensive, especially when we're talking about multiple packages per vehicle. In the future world of Autonomous Electric Vehicle Fleets, the vehicles using LIDAR will get significantly less range efficiency than their radar counterparts and cost significantly more to build. In a fleet scenario where every margin counts this will result in a significant economic pressure to ditch LIDAR. 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. The real competitive advantage for AEV's is in the software, not hardware. LIDAR is a crutch for bad software that reaches a theoretical maximum far short of what is needed for economically-viable LVL5 autonomy. I'll restate this clearly: there's simply no economic or technological advantage to using LIDAR for AEV's.

joshuamorton 7y ago
Lidar doesn't use visible spectrum light. They're usually infrared, so Musk's quote makes no sense. You're making a lot of strong assumptions to draw your conclusions: that commodity cameras and radar can compete on measurement accuracy with lidar systems, and that lidar costs won't decrease with additional investment (we've already seen costs decrease, by like a factor of 10x in less than a decade). The power questions also aren't cut and dry: if you need extra in-vehicle GPUs to support the radar+camera approach, you may well be using more power than a lidar based approach. There's also no real requirement that AVs operate in snow or dense fog. Those are only considerations in certain climates in certain seasons. You don't actually need the safety system to pass the safety tests (that don't currently exist) in worst case conditions if the vehicle works anyway. Why optimize for the worst case first? I'll respond clearly: We're multiple computer vision leaps forward away from what Elon needs for success. They're easily half a decade behind Lidar based systems. And people die as a consequence of putting those systems on the road.
- BluSyn 7y ago
  > The power questions also aren't cut and dry: if you need extra in-vehicle GPUs to support the radar+camera approach, you may well be using more power than a lidar based approach. All current lidar-based approaches I'm aware of also supplement with radar+cameras. LIDAR isn't sufficient in isolation. GPUs consume way too much power, there's no way you can cavalierly just add more of them as a scaling solution. > You don't actually need the safety system to pass the safety tests (that don't currently exist) in worst case conditions if the vehicle works anyway. Why optimize for the worst case first? Not even sure how to interpret such a statement. > I'll respond clearly: We're multiple computer vision leaps forward away from what Elon needs for success. They're easily half a decade behind Lidar based systems. And people die as a consequence of putting those systems on the road. Custom ASICs for ML that Tesla is building is fairly well-understood tech at this point. High-end smartphones have used similar tech for years now (though perhaps an apples-oranges comparison). Only criticism I would level against Tesla's current approach is their overly optimistic time estimates, and hand-waviness about the complexity of solving certain very complicated edge-cases. However their technological approach is quite sound.
  - joshuamorton 7y ago
    > All current lidar-based approaches I'm aware of also supplement with radar+cameras. LIDAR isn't sufficient in isolation. GPUs consume way too much power, there's no way you can cavalierly just add more of them as a scaling solution. Yes, but they don't need to get point-cloud level spacial data from a suite of cameras. That takes more compute power to do at 60fps accurately with cameras than with a lidar. > Not even sure how to interpret such a statement. Let me rephrase: You don't need the vehicles to work in worst case conditions at all, if economically they're still a success if you don't allow them to run in those conditions. If Waymo or Cruise who whomever has self driving taxis in 2020 deployed in temperate cities, and Tesla doesn't have L4 autonomy until 2024, at which point it also works in a blizzard, it doesn't matter if they're cheaper and more effective them. Tesla will have already lost. > Custom ASICs for ML that Tesla is building is fairly well-understood tech at this point. This is one small piece of the puzzle. You also need algorithms that Tesla doesn't appear to have, and (camera) hardware that Tesla claims to have but others seem to agree can't support what they want.
stefco_ 7y ago
> 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.
dmix 7y ago
Ford did some research to improve Lidar for use in the rain/snow using a filtering algorithm. > Ford’s autonomous cars rely on LiDAR sensors that emit short bursts of lasers as they drive along. The car pieces together these laser bursts to create a high-resolution 3D map of the environment. The new algorithm allows the car to analyze those laser bursts and their subsequent echoes to figure out whether they’re hitting raindrops or snowflakes. https://qz.com/637509/driverless-cars-have-a-new-way-to-navigate-in-rain-or-snow/ https://qz.com/637509/driverless-cars-have-a-new-way-to-navi... Still seems less than ideal but I'm curious if that will ever reach somewhere useful.
- mannykannot 7y ago
  FWIW, lidar is very effective in finding the surface below forest canopies. In fact, it will often identify the underbrush, in addition to the canopy top and the surface.
raxxorrax 7y ago
Aren't there still problems with multiple active sensors sweeping the environment? I remember it being a problem in cars that used Lidar but cannot find the info anymore. I think Lidar could still be of help and even the perfect software can use any form of sensory redundancy. But I agree that there might be alternatives. edit: A laser is probably a lot cheaper than camera and imaging dsps if comparable production scales are reached.
stefan_ 7y ago
If all you use is words, it is of course easy to omit that the spatial resolution of radar is barely enough to tell there are one or or two vehicle sized objects in front of you, maybe one to the side, and they better be moving. To compare it to LIDAR is ludicrous.
jayd16 7y ago
>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. Maybe, but there's no reason to leave a local maxima until you actually have something better.

by BluSyn 7y ago

joshuamorton 7y ago

Lidar doesn't use visible spectrum light. They're usually infrared, so Musk's quote makes no sense. You're making a lot of strong assumptions to draw your conclusions: that commodity cameras and radar can compete on measurement accuracy with lidar systems, and that lidar costs won't decrease with additional investment (we've already seen costs decrease, by like a factor of 10x in less than a decade). The power questions also aren't cut and dry: if you need extra in-vehicle GPUs to support the radar+camera approach, you may well be using more power than a lidar based approach. There's also no real requirement that AVs operate in snow or dense fog. Those are only considerations in certain climates in certain seasons. You don't actually need the safety system to pass the safety tests (that don't currently exist) in worst case conditions if the vehicle works anyway. Why optimize for the worst case first? I'll respond clearly: We're multiple computer vision leaps forward away from what Elon needs for success. They're easily half a decade behind Lidar based systems. And people die as a consequence of putting those systems on the road.

BluSyn 7y ago
> The power questions also aren't cut and dry: if you need extra in-vehicle GPUs to support the radar+camera approach, you may well be using more power than a lidar based approach. All current lidar-based approaches I'm aware of also supplement with radar+cameras. LIDAR isn't sufficient in isolation. GPUs consume way too much power, there's no way you can cavalierly just add more of them as a scaling solution. > You don't actually need the safety system to pass the safety tests (that don't currently exist) in worst case conditions if the vehicle works anyway. Why optimize for the worst case first? Not even sure how to interpret such a statement. > I'll respond clearly: We're multiple computer vision leaps forward away from what Elon needs for success. They're easily half a decade behind Lidar based systems. And people die as a consequence of putting those systems on the road. Custom ASICs for ML that Tesla is building is fairly well-understood tech at this point. High-end smartphones have used similar tech for years now (though perhaps an apples-oranges comparison). Only criticism I would level against Tesla's current approach is their overly optimistic time estimates, and hand-waviness about the complexity of solving certain very complicated edge-cases. However their technological approach is quite sound.
- joshuamorton 7y ago
  > All current lidar-based approaches I'm aware of also supplement with radar+cameras. LIDAR isn't sufficient in isolation. GPUs consume way too much power, there's no way you can cavalierly just add more of them as a scaling solution. Yes, but they don't need to get point-cloud level spacial data from a suite of cameras. That takes more compute power to do at 60fps accurately with cameras than with a lidar. > Not even sure how to interpret such a statement. Let me rephrase: You don't need the vehicles to work in worst case conditions at all, if economically they're still a success if you don't allow them to run in those conditions. If Waymo or Cruise who whomever has self driving taxis in 2020 deployed in temperate cities, and Tesla doesn't have L4 autonomy until 2024, at which point it also works in a blizzard, it doesn't matter if they're cheaper and more effective them. Tesla will have already lost. > Custom ASICs for ML that Tesla is building is fairly well-understood tech at this point. This is one small piece of the puzzle. You also need algorithms that Tesla doesn't appear to have, and (camera) hardware that Tesla claims to have but others seem to agree can't support what they want.

stefco_ 7y ago

> 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.

dmix 7y ago

Ford did some research to improve Lidar for use in the rain/snow using a filtering algorithm. > Ford’s autonomous cars rely on LiDAR sensors that emit short bursts of lasers as they drive along. The car pieces together these laser bursts to create a high-resolution 3D map of the environment. The new algorithm allows the car to analyze those laser bursts and their subsequent echoes to figure out whether they’re hitting raindrops or snowflakes. https://qz.com/637509/driverless-cars-have-a-new-way-to-navigate-in-rain-or-snow/ https://qz.com/637509/driverless-cars-have-a-new-way-to-navi... Still seems less than ideal but I'm curious if that will ever reach somewhere useful.

mannykannot 7y ago
FWIW, lidar is very effective in finding the surface below forest canopies. In fact, it will often identify the underbrush, in addition to the canopy top and the surface.

raxxorrax 7y ago

Aren't there still problems with multiple active sensors sweeping the environment? I remember it being a problem in cars that used Lidar but cannot find the info anymore. I think Lidar could still be of help and even the perfect software can use any form of sensory redundancy. But I agree that there might be alternatives. edit: A laser is probably a lot cheaper than camera and imaging dsps if comparable production scales are reached.

stefan_ 7y ago

If all you use is words, it is of course easy to omit that the spatial resolution of radar is barely enough to tell there are one or or two vehicle sized objects in front of you, maybe one to the side, and they better be moving. To compare it to LIDAR is ludicrous.

jayd16 7y ago

>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. Maybe, but there's no reason to leave a local maxima until you actually have something better.