4 ms·

Mentioning the senses wasn't meant to be an itemization of "senses" that a car or car operator needs. Of course you can still drive decently while deaf and with

by aimkey 5y ago

Mentioning the senses wasn't meant to be an itemization of "senses" that a car or car operator needs. Of course you can still drive decently while deaf and without feeling the g-forces. The point is that vision is NOT the only input. In semi modern (non-self-driving) cars, the driver is still assisted by a flurry of additional vehicle sensors in addition to vision and the other human senses. So dismissing LIDAR as "not needed" is foolish. I understand the complexities of modern commercial flight. I still consider it far less complex (computationally) than driving on today's public road. The fact that you have V2V communication out of the box (via transponders) is probably the biggest factor. I would not rule out autonomous driving WITH a standardized V2V / V2E system. Without one? I'm bearish.

kortex 5y ago
My understanding is that LIDAR is diminished on the aspect of not just "not needed," but also potentially exacerbating local minima under good (e.g. fair weather) conditions. Having LIDAR means you can rely on LIDAR, which means you need the LIDAR to be 100% reliable. I've read some companies claiming their lidar works through rain and snow but I haven't seen actual renders of it yet to judge for myself. No lidar acts as a forcing function for Tesla to get their vision system that much more reliable. I think this will ultimately result in a better vision system than the competitors, and I believe that (somewhat paradoxically) this will result in an overall better, safer, and more reliable system, when it comes to the 20% long tail. Lidar+vision will probably dominate in fair weather driving. I'm skeptical it's of much benefit in bad weather, and that long tail is the hard part.
- dragonwriter 5y ago
  > No lidar acts as a forcing function for Tesla to get their vision system that much more reliable. Alternatively, no LIDAR means you aren't gathering corresponding LIDAR and vision data that you can use to improve detection of the vision system.
  - minhazm 5y ago
    Tesla does use LiDAR for calibration and testing of their vision systems. But they also have a million cars on the road with radar which also gives fairly accurate depth information. They use the depth information provided by the radar to automatically label their data and then they can check their vision based estimates against that data. Karpathy has mentioned this approach in a few different talks.

by aimkey 5y ago

kortex 5y ago

My understanding is that LIDAR is diminished on the aspect of not just "not needed," but also potentially exacerbating local minima under good (e.g. fair weather) conditions. Having LIDAR means you can rely on LIDAR, which means you need the LIDAR to be 100% reliable. I've read some companies claiming their lidar works through rain and snow but I haven't seen actual renders of it yet to judge for myself. No lidar acts as a forcing function for Tesla to get their vision system that much more reliable. I think this will ultimately result in a better vision system than the competitors, and I believe that (somewhat paradoxically) this will result in an overall better, safer, and more reliable system, when it comes to the 20% long tail. Lidar+vision will probably dominate in fair weather driving. I'm skeptical it's of much benefit in bad weather, and that long tail is the hard part.

dragonwriter 5y ago
> No lidar acts as a forcing function for Tesla to get their vision system that much more reliable. Alternatively, no LIDAR means you aren't gathering corresponding LIDAR and vision data that you can use to improve detection of the vision system.
- minhazm 5y ago
  Tesla does use LiDAR for calibration and testing of their vision systems. But they also have a million cars on the road with radar which also gives fairly accurate depth information. They use the depth information provided by the radar to automatically label their data and then they can check their vision based estimates against that data. Karpathy has mentioned this approach in a few different talks.