5 ms·

I think you're arguing a strawman? It's not intentionally limiting to match humans - it's recognizing that you need extremely high quality interpretation of vi

I think you're arguing a strawman? It's not intentionally limiting to match humans - it's recognizing that you need extremely high quality interpretation of visual data in order to achieve level 5 and if you have that then you don't need lidar because it has become redundant. If you believe this argument to be true, then investment in lidar is a waste of time at best and at worst it's a distraction from getting the visual interpretation where it needs to be. Arguably companies that focus on it could be 'doomed' because of the investment cost/wrong focus, I think that's what they were getting at. The dense fog is a good counter example - does lidar help in fog? I'd wonder if it would reflect off the fog? Once you've got the visual part working at human level then adding additional lidar probably doesn't hurt (assuming you can get the costs down).

yyyk 6y ago
It's the 'redundant' thing I argue against. I don't agree it's a distraction, it's just more data for the model. More data should be good! There's enough money in this field to invest in everything* , and it's probably easier to add this in advance than to retrofit this in (Surely the retrofitted result would be inferior compared to just having LIDAR from the beginning so that the model can properly account for it). As for dense fog, I am sure that even if LIDAR doesn't work, there's some other kind of no-human-equivalent sensor which does work. [EDIT: A LIDAR-based system to see through fog: https://www.asme.org/topics-resources/content/system-helps-selfdriving-cars-see-fog https://www.asme.org/topics-resources/content/system-helps-s... ] * ok, not everything, but most companies seem to do fine with LIDAR.
- johnydays 6y ago
  Resource allocation plays a part no matter how many resources you have, any company wasting resources is likely to lose to one who doesn't. In the specific case of LIDAR, it also increases the price for the end customer significantly, for negligible ( perhaps reaching 0 at the limit ) gain. More data for the model means a bigger model using more computation and storage, and likely exponentially longer training time.
- fossuser 6y ago
  I think they are using lidar to train the model, but not investing in shipping the sensors to end users. So in that way they probably agree with you more than you think. I’d guess they find that to be the right trade off.

yyyk 6y ago

It's the 'redundant' thing I argue against. I don't agree it's a distraction, it's just more data for the model. More data should be good! There's enough money in this field to invest in everything* , and it's probably easier to add this in advance than to retrofit this in (Surely the retrofitted result would be inferior compared to just having LIDAR from the beginning so that the model can properly account for it). As for dense fog, I am sure that even if LIDAR doesn't work, there's some other kind of no-human-equivalent sensor which does work. [EDIT: A LIDAR-based system to see through fog: https://www.asme.org/topics-resources/content/system-helps-selfdriving-cars-see-fog https://www.asme.org/topics-resources/content/system-helps-s... ] * ok, not everything, but most companies seem to do fine with LIDAR.

johnydays 6y ago
Resource allocation plays a part no matter how many resources you have, any company wasting resources is likely to lose to one who doesn't. In the specific case of LIDAR, it also increases the price for the end customer significantly, for negligible ( perhaps reaching 0 at the limit ) gain. More data for the model means a bigger model using more computation and storage, and likely exponentially longer training time.
fossuser 6y ago
I think they are using lidar to train the model, but not investing in shipping the sensors to end users. So in that way they probably agree with you more than you think. I’d guess they find that to be the right trade off.