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Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

electrek.co

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Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#81

Post by vectorjohn »

maxerickson wrote: post #51
LeifCarrotson wrote: post #32

Earlier quoted context omitted.

That's a big "+" on your figure - it's about 300 Watt-hours per mile , or 3600W constantly, if you're going 60 mph. For comparison, one horsepower is about 750 watts - a Tesla is a very low-drag car. Anandtech reported [1] that the TDP of the whole board is around 250W. The Tegra SoCs are probably around 10W, but that doesn't get you much in the way of GPU horsepower. 1: http://www.anandtech.com/show/9903/nvidia-anno…

Yeah, I screwed up the math. Point stands though.

I don't think it does still stand when you were off by an order of magnitude.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#82

Post by revelation »

ChuckMcM wrote: post #45
Animats wrote: post #20

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…

Unless self driving vehicles drive better than humans, and LIDAR or 360° vision seems a requirement for that, they will not succeed.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#83

Post by rincebrain »

jaffa214525 wrote: post #80
vidarh wrote: post #64

Earlier quoted context omitted.

But the computer vision systems can be endlessly improved and merge experience from millions of cars, while human drivers accumulate experience from a single driver, age, and are eventually replaced by younger, inexperienced drivers. Soon enough these systems will have data from encounters with far more varied situations than any single human will ever be physically able to encounter in a lifetime.

Not sure I see how that would work when there is no 3G signal. If a computer on-board a vehicle sees something it does not recognize when it's not connected to the Tesla HQ, what should it do? And even if it is connected, uploading video over 3G is too slow for the real-time classification needs. Right?

The notion isn't that they phone home to immediately ask about unknown data, but that they periodically feed back any unknown data (or, in the worst case, it gets extracted from a blackbox if a car has an accident), and receive revised models to process the world with.

No, this won't necessarily save you if you see some impossible scenario like a boat cruising down the highway toward you, but it does mean that the list of conditions the model can't respond to reasonably will rapidly trend toward "fewer than most human drivers", in the ideal case.

This will, invariably, have some unfortunate bumps when people discover real-world conditions that, for whatever reason, the model doesn't remotely have responses for (I wonder if they've trained it on e.g. an enormous wall of water, or tornadoes?), but that's why you don't claim it's an always-on self-driving system (e.g. you have to be ready to take over at any point), and arguably the error rate is still going to be lower than most humans to start with.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#84

Post by dzhiurgis »

kchoudhu wrote: post #57
Animats wrote: post #49

Earlier quoted context omitted.

I ran Team Overbot.[1] We were way overdesigned for off-road and underdesigned for going fast. [1] http://www.overbot.com

Same for us -- speed was not a consideration at all. We figured that if we could keep going and finish, we'd be one of the top 3 teams. As it turned out, our mechanical engineering was great. What got us was software: we failed to free memory for passed obstacles, ran into memory exhaustion issues as a result and crashed out at mile 9. When we fixed the leak and reran the course, the truck finished in just over 7 hou…

What was the second time?

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#85

Post by Cybiote »

ChuckMcM wrote: post #45
Animats wrote: post #20

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…

> People drive reasonably well using vision primarily

This is not accurate however. Other important senses in use include proprioceptive, hearing and tactile feedback from wheels. In addition to vision and the improved dynamic range of eyes, there is the important fact that human vision integrates a world model into expectations. Human vision also models time and motion which help manage where to focus attention. Humans can additionally predict other agents and other things about the world based on intuitive physics. This is why they can get on without the huge array of sensors and cars cannot. Humans make up for the lack of sensors by being able to use the poor quality data more effectively.

To put this in perspective, 8.75 megabits / second is estimated to pass through the human retina but only on the order of a 100 bits is estimated to reach conscious attention.

> Computer learning networks can classify imagery at least as accurately as humans and sometimes more so.

This is true but only in a limited sense. For example, when I put in the image on the right (of a car in a swimming pool) from http://icml.cc/2015/invited/LeonBottouICML2015.pdf#page=58 (which you should read and find the talk of but) in ResNet I get as top results:

0.2947; screen, CRT screen

golfcart, golf cart

boathouse

amphibian, amphibious vehicle

For LeNet it's:

0.5422; amphibian, amphibious vehicle

jeep, landrover

wreck

speedboat

The key difference is learning in animals occurs by breaking things down in terms of modular concepts, so even when things are not recognized new things can be labeled as a composition of smaller nearby concepts. Machines cannot yet do this well at all and certainly not as flexibly. Things as lighting and shading do not move animals as much in the concept space.

> 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.

This strategy will not learn from accidents because the signal there will be far from optimal usually.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#86

Post by guiambros »

ChuckMcM wrote: post #45
Animats wrote: post #20

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 don't need LIDAR to drive, why should computers?

That being the case, wouldn't we be limiting self-driving technology to the same traffic-related death rates as humans? Maybe 10, 20% better, but still fundamentally close.

For self-driving cars to be truly successful, the death rates will need to be an order of magnitude better. An incremental improvement won't convince governments and the public at large to trust their lives to an algorithm running inside a black box.

To be an order of magnitude better, you'll likely need to go well beyond simply processing pixels, including LIDAR and other sensors.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#87

Post by onion2k »

TaylorAlexander wrote: post #54
kchoudhu wrote: post #25

Earlier quoted context omitted.

Depth map extraction from vision in real time depends on accurate algorithmic merging of past frames, color gradients and motion vector extraction to come up with a 3D map of what's around the vehicle. Contrast this with LIDAR, which can present a depth map in real time by sending out an array of light pulses and timing how long they take to come back to the IP. Which method is more likely to have implementation erro…

Humans don't use accurate depth maps to drive. Machine learning is great at complex algorithms if done well.

Humans don't use accurate depth maps to drive.

Humans are very good at estimating distance from a combination parallax visual cues and experience. We don't need to have seen a specific model of car before to judge how far away it is with a high level of accuracy.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#88

Post by sushirain »

bcantrill wrote: post #52
ilaksh wrote: post #35

Earlier quoted context omitted.

It's incredibly freaking amazing if they are using deep learning to drive via mainly cameras only 98 percent of the time. No one else can do that. 98 percent is obviously a lot.

Thanks -- that answers the question! So fair to say that it's impressive because of the absence of LIDAR and/or other sensors -- and that by adding LIDAR to such a system one could presumably get towards 0% manual intervention?

The difference between 98% and 99.999% is very difficult to solve, and it's not going to happen in the next 5 years. LIDAR can't help, for example, with obeying a police officer gesture.

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#89

Post by matt_kantor »

Houshalter wrote: post #71
tedunangst wrote: post #50

Earlier quoted context omitted.

ML seems pretty bad at classifying things it hasn't seen before though. There are quite a few examples where an input outside the training data resulted in misclassification. 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?

Classification is not the right metric to use here. Lidar doesn't classify the objects it's looking it, it just tells you the direction and distance. Cameras can also gauge distance pretty effectively from parallax. Either using multiple cameras, or from the motion of the vehicle itself, or both. From this it should be possible to gauge where obstacles are and drive safely. But NNs give the possibility of gathering m…

> But NNs give the possibility of gathering much more information from recognizing objects. Information that Lidar systems don't have.

Wouldn't you feed both the depth map from the lidar and imagery from the cameras into the neural network? I imagine that a variety of different sensors as input would make it easier to do classification. As an analogy, someone who has lost their sense of smell might have a harder time telling the difference between a clean sock and a dirty sock than I would.

Please let me know if I'm wrong here, but I assume that the depth information that can be derived from parallax is not a superset of what you get from lidar (I'm thinking about low light, glare, objects with complicated geometries, similar-colored objects obscuring each other, etc).

Re: Nvidia on new self-driving system: “basically 5 years ahead and coming in 2017”

#90

Post by sushirain »

meow_mix wrote: post #77

This article seems to relate mostly to the computer vision problems associated with self-driving systems, but what about weather conditions? Is this a solved problem?

Weather conditions are a sensor / computer vision /control problem. It is far from solved.
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