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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

#141

Post by Retric »

notahacker wrote: post #140
Retric wrote: post #133

Earlier quoted context omitted.

A bag* or child running into a street is not an usual event, also a car is not going to 'evade' into another car. Rare events are the things people don't see across multiple human lifetimes not just something you don't see every month. Which IMO is what's missing from the debate, unusual events are in terms of ~10+ million miles of training data before these things are in production. They are clearly out there, but I…

> A bag* or child running into a street is not an usual event, also a car is not going to 'evade' into another car Opting to drive around a (stationary, visible from a distance) bag in an unpredictable manner is literally how Waymo's first "at fault" accident occurred... The point is that a human has a concept of a "ball" linked to the concept of "children play football" and an understanding that if one sees the form…

Except, Waymo's is still in the R&D stage where this stuff is being worked out. The bag issue is exactly the kind of thing that will be fixed before production because it's in their training data. It's really the 'unknown unknowns' that will be the problem and they are going to be really odd.

As to the ball case, it does not need to stop just slow if it's possible for a kid to run out fast enough to be a problem. aka on residential streets when it should not be going fast in the first place not a highway where there are no major obstructions. So again, what your describing is in the 'to be dealt with' pile.

I don't mean it's working right now, just they are going to hold off on mass production until it's working.

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

#142

Post by justbaker »

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…

I'm taking notes, go on.

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

#143

Post by EmployedRussian »

notahacker wrote: post #140
Retric wrote: post #133

Earlier quoted context omitted.

A bag* or child running into a street is not an usual event, also a car is not going to 'evade' into another car. Rare events are the things people don't see across multiple human lifetimes not just something you don't see every month. Which IMO is what's missing from the debate, unusual events are in terms of ~10+ million miles of training data before these things are in production. They are clearly out there, but I…

> A bag* or child running into a street is not an usual event, also a car is not going to 'evade' into another car Opting to drive around a (stationary, visible from a distance) bag in an unpredictable manner is literally how Waymo's first "at fault" accident occurred... The point is that a human has a concept of a "ball" linked to the concept of "children play football" and an understanding that if one sees the form…

> Opting to drive around a (stationary, visible from a distance) bag in an unpredictable manner is literally how Waymo's first "at fault" accident occurred...

There wasn't anything unpredictable about the behavior of Google SDC. From the official statement: https://www.engadget.com/2016/02/29/google-self-driving-car-...

"Our car had detected the approaching bus, but predicted that it would yield to us because we were ahead of it.

Our test driver, who had been watching the bus in the mirror, also expected the bus to slow or stop. And we can imagine the bus driver assumed we were going to stay put. Unfortunately, all these assumptions led us to the same spot in the lane at the same time. This type of misunderstanding happens between human drivers on the road every day."

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

#144

Post by pas »

joshuamorton wrote: post #119
ghaff wrote: post #112

Earlier quoted context omitted.

Isn't the practical reality that any course like that will address the theory and essentially deal with more or less toy problems? It would be hard to get into cutting edge research in an online course. I'm not sure learning about natural language processing or image classification would be all that different--although those problems are arguably better understood and more bounded. Even a site reliability engineering…

>Even a site reliability engineering course isn't really going to give you deep insights into what, say, Google does on a day-to-day basis. I think this really gets to the core of the issue. There aren't many courses that will accurately reflect the actual state of the art in any field. Sometimes, graduate courses with a small scope and a relevant professor taught in person can partially address the state of the art,…

Lectures, tech talks, blogs, random experts in relevant subreddits, but yes, an online course is not going to give more than the fundamentals and some advanced, but already well understood examples.

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

#145

Post by jacquesm »

bluGill wrote: post #131
jpindar wrote: post #127

Earlier quoted context omitted.

So during each winter storm, the roads will be lined with cars full of people freezing to death (literally) because their cars decided they can't drive home. And people trying to walk (in the roadway) to shelter, which isn't much safer.

Most cars can stay warm for several days just running the engine at idle. Assuming you get enough fresh air (not always a given in a snow storm that can block air vents) stopping and just running the engine for heat is safe unless your fuel tank is low - which it shouldn't be.

Electric cars can't.

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

#146

Post by theamk »

60654 wrote: post #102
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…

Thank you! It's so good to see hype free discussion of this stuff. I would also add that using simulations to test (or train) robots is also not new, and has significant limitations. Robotics has been using simulation since before the Grand Challenge, but what we find is that simulations make simplifying assumptions that don't bear out in real life: simulated sensors are more idealized than in real life, object recog…

I work in robotics, and I meet people with this attitude all the time. This kind of thinking is OK when your robot is a small desktop toy, or a humanoid robot moving at 1 cm/second, but this approach will not work if your robots can damage expensive equipment or kill people.

The key thing to understand is that your simulator is not a fixed product provided by a third-party. It grows organically with your codebase. So any time you encounter a bug in the real word, you have a three step process:

  - Create a test case to reproduce your bug in the simulation. This may involve updating your simulator to simulate the new physical properties we care about (such as new failure modes or new types of the sensor noise)
  - Fix your code, and make sure your test case passes
  - Add the test case to automatic testing suite and keep it there *forever*. this is the most important part.
For an SDC company, the test suite is almost as important as the code itself. The time spent on writing testing code and test cases could be actually more than time spent writing the driving code itself.

Unfortunately, many academic courses use an opposite approach: they use pre-made simulators, and do not modify them at all. This gives students the wrong expectation that you can find a simulator which can just simulate everything perfectly out-of-the-box; when this (predictably) fails, they become disappointed in the whole simulation idea.

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