41–50 of 174 posts
Earlier quoted context omitted.
Many people. And I still don't expect to see general-purpose (secondary roads/cities, range of weather conditions) for decades.
This exactly. I did robotics in the late 90's and it's interesting to see how many fundamental problems still remain unsolved. And the trajectory of tech advancement is positive for sure, but not _nearly_ as fast as popsci and marketing articles suggest.
Earlier quoted context omitted.
Many people. And I still don't expect to see general-purpose (secondary roads/cities, range of weather conditions) for decades.
This exactly. I did robotics in the late 90's and it's interesting to see how many fundamental problems still remain unsolved. And the trajectory of tech advancement is positive for sure, but not _nearly_ as fast as popsci and marketing articles suggest.
Earlier quoted context omitted.
Wait, who cares about the power consumption in a car? perf/W is a useful number if you are trying to stack a data center full of these things and electricity (incl for cooling) is essentially your only cost. But if a car is using 300Wh/mile or, in an ICE, generating 100kW in excess heat it is an entirely pointless metric. (Just for clarification: no one is using the Drive PX2 in a data center. Just look at these conn…
Informal anecdotal knowledge: GM wants 10-20 watts for the entire self-driving system, sensors and all.
But in the meantime, where we haven't actually solved the problem even when the trunk is full of racks with high-end boxes, it seems silly to mandate that, in the classic GM "compromises safety" sense.
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Stereo adds just another layer of data to merge, yeah. I should add a disclaimer: I'm waaaaay out of date. The last time I worked on this stuff was back in 2005 during the DARPA Grand Challenge. And while I'm fairly confident that the basics are still the same (the fact that we're still discussing monocular vs. stereo vs. LIDAR is testament to that...), I'm going to defer to someone more up-to-date to provide more re…
Yeah, me too; I was in the 2005 Grand Challenge. Vision processing has made enormous progress since then. Here's Mobileye's guy explaining what they do and what needs to be done.[1] Here's the NVidia guy.[2] Here's Chris Urmson from Google.[3] All three use their vision system to draw boxes around things that look like obstacles. Then the planner uses those boxes to construct a path. The vision system just gives you…
I'm still not happy with self-driving on vision alone, or vision augmented with radar. There are too many hard cases for vision. Everybody who has good self-driving right now - Google, Otto, Volvo, GM - uses LIDAR. Self-driving is coming to the first end users in 2017, in Volvo's test of 100 vehicles. Volvo has multiple LIDARs, multiple radars, multiple cameras, redundant computers, and redundant actuators. They're b…
People drive reasonably well using vision primarily and with imperfect visibility of their environment.
Computer learning networks can classify imagery at least as accurately as humans and sometimes more so.
A computer using imagery that is well classified from an array of visual sensors with near perfect visibility should be able to drive as well, or better, than a human driver.
The execution strategy appears to be to run classification and command prediction all the time, and while the human is in control consider it supervised learning.
The argument against LIDAR is just this in reverse, humans don't need LIDAR to drive, why should computers?
LIDAR is an engineering solution to the problem of creating a representation of the 3D space around the vehicle. It is a stand in for the less well understood human ability to do the same just by looking around. As a result if the "looking around" solution being proposed by NVidia and Tesla meets the engineering requirement, I don't see any reason that the car should have LIDAR.
Who thought making decent autonomous cars was going to take as long as 2020? I mean for regulations etc maybe, but not for the tech. Earlier this year I expected it to be done later this year. Edited for clarity (I hope).
As someone not living a place with huge, straight highways with perfect asphalt, I haven't seen anything making me believe it will be generally available soon. Right now I'm in the mountains. Narrow, icy roads. Thick snow fall, making some of the sensors already available unreliable. The Google approach is to map everything before hand, and mostly easy conditions. Tesla somewhat more general, but still not too diffic…
Who thought making decent autonomous cars was going to take as long as 2020? I mean for regulations etc maybe, but not for the tech. Earlier this year I expected it to be done later this year. Edited for clarity (I hope).
In general, people tend to overestimate technological advancement in the short term.
Earlier quoted context omitted.
Yeah, me too; I was in the 2005 Grand Challenge. Vision processing has made enormous progress since then. Here's Mobileye's guy explaining what they do and what needs to be done.[1] Here's the NVidia guy.[2] Here's Chris Urmson from Google.[3] All three use their vision system to draw boxes around things that look like obstacles. Then the planner uses those boxes to construct a path. The vision system just gives you…
Aww yeah -- another Challenger! What team were you on?
I'm still not happy with self-driving on vision alone, or vision augmented with radar. There are too many hard cases for vision. Everybody who has good self-driving right now - Google, Otto, Volvo, GM - uses LIDAR. Self-driving is coming to the first end users in 2017, in Volvo's test of 100 vehicles. Volvo has multiple LIDARs, multiple radars, multiple cameras, redundant computers, and redundant actuators. They're b…
The argument reasoning I've heard goes like this; People drive reasonably well using vision primarily and with imperfect visibility of their environment. Computer learning networks can classify imagery at least as accurately as humans and sometimes more so. A computer using imagery that is well classified from an array of visual sensors with near perfect visibility should be able to drive as well, or better, than a h…
Humans may not always see a white truck in a snowstorm, but is computer vision going to see it either? Or will it pattern match the few visible parts as something else entirely? Or dismiss the truck entirely as noise?