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Inside Waymo's Secret World for Training Self-Driving Cars

theatlantic.com

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Re: Inside Waymo's Secret World for Training Self-Driving Cars

#91

Post by dsfyu404ed »

flamedoge wrote: post #54
bit_logic wrote: post #48

Self driving cars is based on machine learning which is basically processing massive amounts of past data. This is great for routine situations and it seems Waymo is making good progress covering most of these. However the key weakness is the lack of true intelligence. When anything unusual or unexpected happens, the best it can do is simply safely shutdown and wait for a human to intervene. And the car can't really…

I can see how obtaining training data is difficult. majority of data would be non-accidents.

It's the near misses that matter because eventually you can figure out why those ones missed.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#92

Post by whipoodle »

joering2 wrote: post #75

Great to see such progress made by Waymo and Co. But as the Murphy's law says, if something might break, it will eventually break. With possibly millions of self driving cars and trillions of unique situations, its inevitable that someone will get hurt. So my question is: what kind of progress is being made to draft a legal framework for situation in which I rode my bike in bike lane and for whatever reason self-driv…

To me it just has to be better than human drivers, who are actually pretty awful. I think it's unfortunate that we all seem focused on having self-driving cars be basically perfect. If they were only twice as good as human drivers we should jump at the chance to reduce fatalities.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#93

Post by dsfyu404ed »

kylegordon wrote: post #70
0xB31B1B wrote: post #36

Earlier quoted context omitted.

Wow, and I must think we're looking to squeeze ~3 more orders of magnitude or more of the reliability before going full driverless ~(1m miles/disengagement)

I wonder how many disengagements per unit of distance your average human has...

If you ignore low speed, low visibility parking lot stuff many people will go their whole lives without being in an accident that they are at least partially responsible for.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#94

Post by lightbyte »

jooke wrote: post #64
flamedoge wrote: post #57

> But Peng also presented the position of the traditional automakers. He said that they are trying to do something fundamentally different. Instead of aiming for the full autonomy moon shot, they are trying to add driver-assistance technologies, “make a little money,” and then step forward toward full autonomy. It’s not fair to compare Waymo, which has the resources and corporate freedom to put a $70,000 laser range…

Who's going to buy a car that might tell them they can't drive?

People that get convicted of DUIs and want to keep their license. We already have modifications to cars that require people to blow into to start their car, this seems like the logical next step in that.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#95

Post by nradov »

peoplewindow wrote: post #58
dlubarov wrote: post #13

Earlier quoted context omitted.

Agreed. As a Google engineer, I think a lot of our AI efforts are "safe" (as in we'll keep investing in them for a long time) because they're already providing substantial business value. For example, - TTS and speech synthesis have lots of uses in Android (phones, Wear, Auto, etc.) - Object recognition is very useful for photo search - Face recognition is also useful for photo search (if you tag people in Google Pho…

I doubt Google will cut back any time soon, but as a former Google engineer, I find this an entertaining definition of business value ;) How much money does Google Photos make? Last time I used it, there were no ads in it. Isn't speech recognition nearly at human levels of comprehension, at least for US English and outside of highly technical jargon? Speech patterns change slowly, so would Android continue to make mo…

Speech recognition is still nowhere near human levels of comprehension. I have no speech impediments and speak US English with a neutral accent, yet Android speech recognition makes so many mistakes that it's usually faster to just type.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#97

Post by skywhopper »

I'm disappointed the article wasn't a bit more skeptical of some of the claims. Certainly the simulation-based testing is a good thing, but stats about how many billions of simulated miles have been driven can create a self-reinforcing delusion if everyone involved isn't careful to remember that the simulations can only work with well-known and expected situations. It sounds like Waymo realizes this and is building a huge library of scenarios to evaluate, but one million simulated miles are not worth a hundred real miles in terms of confidence in the system, and that is not the message this article portrays.

Overall this article does give hints though that autonomous vehicles are truly much further away than anyone wants to admit. The story about being flummoxed by multilane roundabouts is depressing. If they didn't know such things existed then they were incredibly sloppy in their data gathering. If they truly thought scaling from a simple roundabout to a multilane one would be trivial then their staff don't have the right mindset for this problem space.

Also note that all the testing is happening in flat, desert landscapes where there are no weather or lighting challenges. Their pictured model residential street only has stubs of driveways. No houses, no trees. I'm sure they know these are gaps but I worry they underestimate the challenges of adapting to entirely different driving environments. Especially when machine learning is involved and you're simulating 99% of your mileage...

Looking forward to checking back in 2040 though.

Re: Inside Waymo's Secret World for Training Self-Driving Cars

#98

Post by spyspy »

kozhevnikov wrote: post #79
Animats wrote: post #46

There's a lot of misunderstanding about self-driving. Mostly because nobody is publishing much. If you want to do it right, you start with geometry. The first step is capturing range imagery and grinding it down to a 3D model of the world. This tells you where you physically can go. That's where we were at the DARPA Grand Challenge over a decade ago. Then comes moving object popout. What out there isn't a stationary…

Chris Urmson's SXSW video: https://youtu.be/Uj-rK8V-rik

Timestamp for duck part https://youtu.be/Uj-rK8V-rik?t=26m11s
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