3 ms·

> Realizing that, for a LIDAR with a 400' range at 15hz moving at 40mph you get ~7 samples of a point before you're at it... For at least the first 3 frames tha

> Realizing that, for a LIDAR with a 400' range at 15hz moving at 40mph you get ~7 samples of a point before you're at it... For at least the first 3 frames that person is going to look like sensor noise. At 110 feet that person (which I'm calling a 2' wide target) is 1 degree of your sensor measurement. Updating this with math not done at midnight: Frame Distance Angular Size of Target Pixel Size 0 400 0.286481285 7.162032123 1 396.0888889 0.289310143 7.232753564 2 388.2666667 0.295138837 7.378470932 3 376.5333333 0.304335969 7.608399215 4 360.8888889 0.317529122 7.93822806 5 341.3333333 0.3357213 8.393032503 6 317.8666667 0.360506726 9.012668144 7 290.4888889 0.394484519 9.862112983 8 259.2 0.442105838 11.05264595 9 224 0.511583054 12.78957636 10 184.8888889 0.619810252 15.4952563 11 141.8666667 0.807794769 20.19486922 12 94.93333333 1.207252619 30.18131548 13 44.08888889 2.600887175 65.02217938 This is based on the velodyne LIDAR specs I could find last night with some quick googling: - 400' range - .04 degree angular resolution - 15hz max update rate If you have more accurate real world experience with these sensors and can share more accurate performance characteristics I can update. These calculations were done assuming a vehicle moving at 40 mph. The stopping distance at that speed is about 110ft. I computed the pixel size by assuming 1 measurement = 1 pixel giving me 9000 pixels per 360 degrees.

rightbyte 9y ago
I think you are using the wrong angular resolution, though. http://velodynelidar.com/docs/datasheet/63-9194_Rev-G_HDL-64E_S3_Spec%20Sheet_Web.pdf http://velodynelidar.com/docs/datasheet/63-9194_Rev-G_HDL-64... Thats the one LIDAR Uber seems to have matching pictures. 5Hz - 20Hz full round sampling rate, lets assume 15 Hz. The resolution in the horizontal plane is dependent on rotational speed, so at 15 Hz it should be 0,26 degrees. (0,35/20*15 = 0.26) For the woman height the angular resolution is 0.4 degrees no matter the rotation speed. Id est, she would have been atleast one pixel wide from 400 feet and about 2 pixels high and growing in size if we assume 2' wide. (Not counting bike). I really see no exuse for Uber messing this up that bad. The LIDAR can't have missed a potential "obstacle" when it got closer, even if the car wouldn't classify it as a human.
- schreiaj 9y ago
  I was using Rev E because it's the data sheet I had handy. Mostly I was trying to point out that LIDAR is not some magic thing that always sees everything and there's limitations. Frame Distance Angular Size of Target Pixel Size 0.00 400.00 0.29 1.10 1.00 396.09 0.29 1.11 2.00 388.27 0.30 1.14 3.00 376.53 0.30 1.17 4.00 360.89 0.32 1.22 5.00 341.33 0.34 1.29 6.00 317.87 0.36 1.39 7.00 290.49 0.39 1.52 8.00 259.20 0.44 1.70 9.00 224.00 0.51 1.97 10.00 184.89 0.62 2.38 11.00 141.87 0.81 3.11 12.00 94.93 1.21 4.64 13.00 44.09 2.60 10.00 There's with your .26 angular resolution @ 15hz. (I just have a spreadsheet that spits all these out for me.) These are NOT big targets, they could easily have been mistaken for noise and filtered out. All of the LIDAR data I've ever seen has been fairly noisy and did require filtering to get usable information from it. And given the number of frames they get maybe their filtering was just too aggressive.
  - rightbyte 9y ago
    Yes, I agree with you that we can't assume that the car could have noticed the woman from 120 meters from LIDAR data alone. Maybe with some kind of sensor fusion with IR-cameras. But, as it got closer and what the computer though was noise was on about the same place a sane obstacle finder should have given a posetive match. Maybe at 30 - 40 m worst case? At 142 feet the woman probably had (assuming she was 5.5'): asind(5.5/142) = 2.21* => 2.21/0.4 = 5.5 So between 5 and 6 "scanlines" going from left to right over her. Assuming she was 2' wide that's 0.8 degrees which would be 2 to 3 pixels in breadth according to your spread sheet. That's between 10 and 18 pixels (voxels?) that stand out clearly from the flat road around it, exluding the bike. If you wan't to get an idea of how LIDAR data looks Velodyne has free samples and a viewer for less resolution models. http://velodynelidar.com/downloads.html http://velodynelidar.com/downloads.html It pretty hard to identify obstacles far off, but you will still see there is something there. It's especially easy to identify obstacles that are vertical. As she got closer, she would eventually show up clearly on the LIDAR data. But since the car never slowed down or went left, it didn't notice her at all even at point blanc (or did see her but failed to do anything about it).
    - 9y ago

rightbyte 9y ago

I think you are using the wrong angular resolution, though. http://velodynelidar.com/docs/datasheet/63-9194_Rev-G_HDL-64E_S3_Spec%20Sheet_Web.pdf http://velodynelidar.com/docs/datasheet/63-9194_Rev-G_HDL-64... Thats the one LIDAR Uber seems to have matching pictures. 5Hz - 20Hz full round sampling rate, lets assume 15 Hz. The resolution in the horizontal plane is dependent on rotational speed, so at 15 Hz it should be 0,26 degrees. (0,35/20*15 = 0.26) For the woman height the angular resolution is 0.4 degrees no matter the rotation speed. Id est, she would have been atleast one pixel wide from 400 feet and about 2 pixels high and growing in size if we assume 2' wide. (Not counting bike). I really see no exuse for Uber messing this up that bad. The LIDAR can't have missed a potential "obstacle" when it got closer, even if the car wouldn't classify it as a human.

schreiaj 9y ago
I was using Rev E because it's the data sheet I had handy. Mostly I was trying to point out that LIDAR is not some magic thing that always sees everything and there's limitations. Frame Distance Angular Size of Target Pixel Size 0.00 400.00 0.29 1.10 1.00 396.09 0.29 1.11 2.00 388.27 0.30 1.14 3.00 376.53 0.30 1.17 4.00 360.89 0.32 1.22 5.00 341.33 0.34 1.29 6.00 317.87 0.36 1.39 7.00 290.49 0.39 1.52 8.00 259.20 0.44 1.70 9.00 224.00 0.51 1.97 10.00 184.89 0.62 2.38 11.00 141.87 0.81 3.11 12.00 94.93 1.21 4.64 13.00 44.09 2.60 10.00 There's with your .26 angular resolution @ 15hz. (I just have a spreadsheet that spits all these out for me.) These are NOT big targets, they could easily have been mistaken for noise and filtered out. All of the LIDAR data I've ever seen has been fairly noisy and did require filtering to get usable information from it. And given the number of frames they get maybe their filtering was just too aggressive.
- rightbyte 9y ago
  Yes, I agree with you that we can't assume that the car could have noticed the woman from 120 meters from LIDAR data alone. Maybe with some kind of sensor fusion with IR-cameras. But, as it got closer and what the computer though was noise was on about the same place a sane obstacle finder should have given a posetive match. Maybe at 30 - 40 m worst case? At 142 feet the woman probably had (assuming she was 5.5'): asind(5.5/142) = 2.21* => 2.21/0.4 = 5.5 So between 5 and 6 "scanlines" going from left to right over her. Assuming she was 2' wide that's 0.8 degrees which would be 2 to 3 pixels in breadth according to your spread sheet. That's between 10 and 18 pixels (voxels?) that stand out clearly from the flat road around it, exluding the bike. If you wan't to get an idea of how LIDAR data looks Velodyne has free samples and a viewer for less resolution models. http://velodynelidar.com/downloads.html http://velodynelidar.com/downloads.html It pretty hard to identify obstacles far off, but you will still see there is something there. It's especially easy to identify obstacles that are vertical. As she got closer, she would eventually show up clearly on the LIDAR data. But since the car never slowed down or went left, it didn't notice her at all even at point blanc (or did see her but failed to do anything about it).
  - 9y ago