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Personally, I don't think Lidar is needed to make self-driving cars. Otherwise we have a problem since humans do not come with them to date. The problem I hav

Personally, I don't think Lidar is needed to make self-driving cars. Otherwise we have a problem since humans do not come with them to date. The problem I have with the article is he said Lidar will never come done in costs to that of a headlight. Tech marches on and headlights are not really that cheap, in the future I would not be surprised with mass production that Lidar does get that cheap. How much did the first Kim-1 computers cost? How does that compare to a simple Arduino with a hundred times the power and memory cost today?

bluGill 8y ago
Humans are not safe drivers. If Lidar makes the autonomous care safer than humans I want lidar.
- kwhitefoot 8y ago
  > Humans are not safe drivers I see this repeated over and over in this thread, elsewhere on HN, and in many other places on the web. But no one explains what they mean by a safe driver. There are other factors at work than mere human-ness. For instance the US has more than double the number of fatalities per unit vehicle distance than the UK, more than triple per head of population. In both countries the driver is a human yet the disparity in outcome is huge. Perhaps whatever it is that causes this sort of difference should be addressed rather than insisting on a new an inherently unproven technology. Or, at least, give us some numbers so that we can tell what you mean by safe. See https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate https://en.wikipedia.org/wiki/List_of_countries_by_traffic-r...
  - bluGill 8y ago
    Even in the UK there are deaths. Yes the US should work on whatever it is that makes UK drivers safer, but UK drivers still kill people and are thus unsafe. You have just given numbers: when autonomous cars beat those numbers they are better. Until then we should be careful.
KaiserPro 8y ago
> I don't think Lidar is needed to make self-driving cars. The only sensor that can give mm accurate, high resolution, long range 3d spacial data with a low latency, is lidar. For a purely visual system to supplant lidar we need: 1) a self cleaning all weather/all light condition camera with ~170 degree field of view at ~30 megapixels 2) a Structure from motion system that has a sub 20ms latency, and can work with spherical images, at full resolution. 3) a semantic object recognising system that is able to classify any object class. It must be scale, rotation, colour and occlusion invariant. It must also update the worl map generated by 2 4) An object threat management system, that can take semantic information from 3, rank it in order of threat, to be passed to 5 5) A world motion prediction engine, that takes threats from 4 and works out if said object is likely to collide with the car all inside 70-100ms Thats not going to happen soon. Of the whole list, 2 is the closest to working. Lidar allows you to cut through 90% of this, because it gives you an accurate point cloud. From there you can do clustering to make objects, and track those clusters to measure threats. All of this is doable on a small CPU now. Without AI.
- sliken 8y ago
  You make lidar sound great, much better than I've heard. I thought lidar was quite sensitive to dust, rain, snow, and fog. This is particularly worrisome because lidar samples at a MUCH lower rate than a camera (from 30M samples per cheap camera to 300,000 or so for an expensive lidar). While lidar is pretty impressive at short ranges, what about at 2 seconds away @ 65 MPH? Will it detect a deaccelerating car faster than a camera that can detect a brake light? Will it be better at detecting perpendicular cars vs parked cars at that range? Will weather cause the lidar to decay in similar ways to human eyes?
  - KaiserPro 8y ago
    >what about at 2 seconds away @ 65 MPH? 65 MPH is about 30m/s, your average lidar should be good upto about 200 meters, which is about 6 seconds at 65 mph. > Will it detect a de-accelerating car faster than a camera that can detect a brake light? Now this is an interesting question. yes and no. A lidar will on its own will not give you object recognition. It will tell you that a reflective surface of size _n_ is directly in you proposed path, and that since last scan its got closer. From that you can make a very accurate obsticle map. I don't think that running a car soely on Lidar is actually all that feasible or a clever idea. Not without a boat load of mapping and processing to create a machine readable semantic map first. Having a camera array _as well as_ lidar is a very good idea, as it can provide blended information from the lidar to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.

bluGill 8y ago

Humans are not safe drivers. If Lidar makes the autonomous care safer than humans I want lidar.

kwhitefoot 8y ago
> Humans are not safe drivers I see this repeated over and over in this thread, elsewhere on HN, and in many other places on the web. But no one explains what they mean by a safe driver. There are other factors at work than mere human-ness. For instance the US has more than double the number of fatalities per unit vehicle distance than the UK, more than triple per head of population. In both countries the driver is a human yet the disparity in outcome is huge. Perhaps whatever it is that causes this sort of difference should be addressed rather than insisting on a new an inherently unproven technology. Or, at least, give us some numbers so that we can tell what you mean by safe. See https://en.wikipedia.org/wiki/List_of_countries_by_traffic-related_death_rate https://en.wikipedia.org/wiki/List_of_countries_by_traffic-r...
- bluGill 8y ago
  Even in the UK there are deaths. Yes the US should work on whatever it is that makes UK drivers safer, but UK drivers still kill people and are thus unsafe. You have just given numbers: when autonomous cars beat those numbers they are better. Until then we should be careful.

KaiserPro 8y ago

> I don't think Lidar is needed to make self-driving cars. The only sensor that can give mm accurate, high resolution, long range 3d spacial data with a low latency, is lidar. For a purely visual system to supplant lidar we need: 1) a self cleaning all weather/all light condition camera with ~170 degree field of view at ~30 megapixels 2) a Structure from motion system that has a sub 20ms latency, and can work with spherical images, at full resolution. 3) a semantic object recognising system that is able to classify any object class. It must be scale, rotation, colour and occlusion invariant. It must also update the worl map generated by 2 4) An object threat management system, that can take semantic information from 3, rank it in order of threat, to be passed to 5 5) A world motion prediction engine, that takes threats from 4 and works out if said object is likely to collide with the car all inside 70-100ms Thats not going to happen soon. Of the whole list, 2 is the closest to working. Lidar allows you to cut through 90% of this, because it gives you an accurate point cloud. From there you can do clustering to make objects, and track those clusters to measure threats. All of this is doable on a small CPU now. Without AI.

sliken 8y ago
You make lidar sound great, much better than I've heard. I thought lidar was quite sensitive to dust, rain, snow, and fog. This is particularly worrisome because lidar samples at a MUCH lower rate than a camera (from 30M samples per cheap camera to 300,000 or so for an expensive lidar). While lidar is pretty impressive at short ranges, what about at 2 seconds away @ 65 MPH? Will it detect a deaccelerating car faster than a camera that can detect a brake light? Will it be better at detecting perpendicular cars vs parked cars at that range? Will weather cause the lidar to decay in similar ways to human eyes?
- KaiserPro 8y ago
  >what about at 2 seconds away @ 65 MPH? 65 MPH is about 30m/s, your average lidar should be good upto about 200 meters, which is about 6 seconds at 65 mph. > Will it detect a de-accelerating car faster than a camera that can detect a brake light? Now this is an interesting question. yes and no. A lidar will on its own will not give you object recognition. It will tell you that a reflective surface of size _n_ is directly in you proposed path, and that since last scan its got closer. From that you can make a very accurate obsticle map. I don't think that running a car soely on Lidar is actually all that feasible or a clever idea. Not without a boat load of mapping and processing to create a machine readable semantic map first. Having a camera array _as well as_ lidar is a very good idea, as it can provide blended information from the lidar to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.