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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”

#101

Post by dogma1138 »

cambion wrote: post #98
dogma1138 wrote: post #72

Earlier quoted context omitted.

They aren't buying consumer GPU's they aren't buying the NVIDIA dedicated servers, but they aren't running Geforce chips either. If nothing else is that because you cannot virtualize Geforce line GPU's, there is no CUDA Direct or NVLINK support etc. If you are telling me that Google is buying Geforce GPU's and flashing the bios with a custom bios ripped off a Quadro card so they can do PCIe passthrough in a hyperviso…

While I agree that Google is not buying GeForce GPUs, their general use-case for GPUs does not require virtualization. They use containers to isolate and throttle different tasks/jobs running on the same hardware. At their scale, virtualization would be significantly wasteful in terms of manageability and overhead.

I think we have different meaning for virtualization when it comes to GPU.

I'm not talking about running virtual OS, I'm talking about things like rCUDA, GPU direct and RDMA.

But still even for their containers solution they need support for gpu passtrough and vGPU if not they can't run containers.

NVIDIA doesn't allow you to run GeForce cards over a hypervisor.

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

#102

Post by enraged_camel »

Cybiote wrote: post #85
ChuckMcM wrote: post #45

Earlier quoted context omitted.

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. Hum…

>>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.

Yes, but all those things you listed are very imperfect and can increase the risk of mistakes. For example, someone two lanes over honking their horn can cause a momentary distraction for you ("are they honking at me?"), causing you to not notice a cyclist cutting in front of you. And so can a dancing clown on the sidewalk.

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

#104

Post by kchoudhu »

dzhiurgis wrote: post #84
kchoudhu wrote: post #57

Earlier quoted context omitted.

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?

I ran across quite a few of them while working tech at an investment bank. Never anything that resulted in direct losses, but implied missed revenue -- definitely.

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

#105

Post by lightcatcher »

mattnewton wrote: post #59
dogma1138 wrote: post #53

Earlier quoted context omitted.

>I'm not a big fan of $150K high-end servers filled with $5000 GPUs that can be bested with clever code on a $25K server fill with $1200 consumer GPUs. But I am a huge fan of charging what you can while you are unopposed. It's just that I think that state is temporary. There is virtually not a single "enterprise" grade product which can't be made at least 50% cheaper (or sometimes 10 times...) with off the shelf cons…

I don't understand your argument, so maybe this is off base, but if you are saying people in industry aren't replacing their supercomputers with commodity gpu's, you're wrong; both apple and google have massive purchase orders for commodity nvidia gpus because they aren't just cheaper, they are better at this application. And I imagine other companies are as well. Edit: "replace" is probably not the right word, this…

> both apple and google have massive purchase orders for commodity nvidia gpus

source?

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

#106

Post by agumonkey »

onion2k wrote: post #87
TaylorAlexander wrote: post #54

Earlier quoted context omitted.

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.

Reminds me how much I hate the average mirror. They force me to lost frontal depth by focusing on sides. I tried driving while looking aside for a minute, it's impressive how much your peripheral vision is meaningless then, even at 5mph you're never sure how far the car before you is.

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

#107

Post by sliken »

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…

Tesla is going a different way with radar+cameras. Lidar TODAY is too expensive for normal priced vehicles, google's solution is very expensive, volvo seems to be targeting large trucks which are less price sensitive. The price of the future volvo passenger cars hasn't been announced, has it?

Society seems hyper sensitive to different risks, even if lower than existing risks. Thus a tesla fire is big news, even if the rate is less than the normal gas car fires. Thus weaknesses of lidar (like say fog) could cause problems, even if safer than existing cars. This is complicated by the humans driving cars around. Imagine heavy fog on the highway, and that humans decide that 45mph is safe, and the telsa (with a camera+radar system) decides on similar. Lidar might well decide the safe speed is less, and get rear ended more.

Reference for the 3 rammed cars "at speed"? The one I saw the car in front slowed, then accelerated, and merged right before a stopped car. The telsa slowed, then accelerated, decided it wasn't safe to merge, and braked hard. It did hit the car, but not very fast. Not sure I would have done better myself.

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

#108

Post by viraptor »

Cybiote wrote: post #85
ChuckMcM wrote: post #45

Earlier quoted context omitted.

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. Hum…

> This is true but only in a limited sense.

Isn't what we need very limited anyway? What the cars need is recognition of obstacles and the type of the obstacle in a very limited range. Basically when something is on the road, it doesn't matter whether it's a moose or a deer - you slow down and avoid, or brake depending on the environment.

"what is in this picture" classifiers don't seem like a good algorithm to use in that case. Object detection / feature extraction seems to be much closer.

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

#109

Post by dave_sullivan »

Hydraulix989 wrote: post #95
dave_sullivan wrote: post #94

Earlier quoted context omitted.

> 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. Actually, that's pretty much what deep learning is doing. For instance: https://papers.nips.cc/paper/5027-zero-shot-learning-through..…

Well, not with traditional feedforward networks (LeNet, etc.). You can't run the classifier and find tires, then wheels, and then a car; but you do get composition of features.

> not with traditional feedforward networks (LeNet, etc.)

I'd argue they are implicitly doing this.

> You can't run the classifier and find tires, then wheels, and then a car;

Why can't you run a classifier for tires, one for wheels, one for cars, then combine their outputs for a final classifier maybe based on a decision tree? You can train all the networks at the same time and it will give you a probability distributions for all 4 outputs (tires, wheels, cars, blended). What am I missing?

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

#110

Post by sliken »

Shivetya wrote: post #65
ChuckMcM wrote: post #45

Earlier quoted context omitted.

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…

LIDAR is just another form of seeing, just not as we are used to as people but combined with cameras they two would compliment each other. Relying on only one is a fool's gambit. LIDAR won't go blind from white trucks on sunny days. LIDAR won't suffer snow blindness or inability to track in conditions where humans don't see well, like heavy rain at night. You add in visual acquisition to fine tune what you are detect…

Actually in snow, fog, rain, and related humans (and cameras) can do pretty reasonably. Things like brake lights and running lights can be pretty severely distorted and you still know the approximate distance to the car in front of you.

Lidar on the other hand is a point source of light (not from the environment) and any distortion makes it less likely for said light to return to the sensor. So with stereo vision (or radar) you can get a relatively accurate distance for a car in front of you. With lidar some fraction of the returns will be bouncing off fog/snow/rain between you and the distant object.

Because of this disadvantage lidar based systems might suggest a slower safe speed, and risk rear ending from humans.

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