Chris Urmson: How a driverless car sees the road

863,108 views ・ 2015-06-26

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Please double-click on the English subtitles below to play the video.

00:12
So in 1885, Karl Benz invented the automobile.
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Later that year, he took it out for the first public test drive,
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and -- true story -- crashed into a wall.
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For the last 130 years,
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we've been working around that least reliable part of the car, the driver.
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We've made the car stronger.
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We've added seat belts, we've added air bags,
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and in the last decade, we've actually started trying to make the car smarter
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to fix that bug, the driver.
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Now, today I'm going to talk to you a little bit about the difference
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between patching around the problem with driver assistance systems
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and actually having fully self-driving cars
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and what they can do for the world.
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I'm also going to talk to you a little bit about our car
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and allow you to see how it sees the world and how it reacts and what it does,
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but first I'm going to talk a little bit about the problem.
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And it's a big problem:
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1.2 million people are killed on the world's roads every year.
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In America alone, 33,000 people are killed each year.
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To put that in perspective,
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that's the same as a 737 falling out of the sky every working day.
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It's kind of unbelievable.
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Cars are sold to us like this,
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but really, this is what driving's like.
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Right? It's not sunny, it's rainy,
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and you want to do anything other than drive.
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And the reason why is this:
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Traffic is getting worse.
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In America, between 1990 and 2010,
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the vehicle miles traveled increased by 38 percent.
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We grew by six percent of roads,
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so it's not in your brains.
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Traffic really is substantially worse than it was not very long ago.
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And all of this has a very human cost.
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So if you take the average commute time in America, which is about 50 minutes,
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you multiply that by the 120 million workers we have,
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that turns out to be about six billion minutes
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wasted in commuting every day.
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Now, that's a big number, so let's put it in perspective.
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You take that six billion minutes
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and you divide it by the average life expectancy of a person,
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that turns out to be 162 lifetimes
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spent every day, wasted,
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just getting from A to B.
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It's unbelievable.
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And then, there are those of us who don't have the privilege
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of sitting in traffic.
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So this is Steve.
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He's an incredibly capable guy,
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but he just happens to be blind,
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and that means instead of a 30-minute drive to work in the morning,
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it's a two-hour ordeal of piecing together bits of public transit
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or asking friends and family for a ride.
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He doesn't have that same freedom that you and I have to get around.
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We should do something about that.
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Now, conventional wisdom would say
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that we'll just take these driver assistance systems
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and we'll kind of push them and incrementally improve them,
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and over time, they'll turn into self-driving cars.
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Well, I'm here to tell you that's like me saying
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that if I work really hard at jumping, one day I'll be able to fly.
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We actually need to do something a little different.
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And so I'm going to talk to you about three different ways
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that self-driving systems are different than driver assistance systems.
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And I'm going to start with some of our own experience.
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So back in 2013,
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we had the first test of a self-driving car
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where we let regular people use it.
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Well, almost regular -- they were 100 Googlers,
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but they weren't working on the project.
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And we gave them the car and we allowed them to use it in their daily lives.
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But unlike a real self-driving car, this one had a big asterisk with it:
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They had to pay attention,
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because this was an experimental vehicle.
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We tested it a lot, but it could still fail.
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And so we gave them two hours of training,
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we put them in the car, we let them use it,
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and what we heard back was something awesome,
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as someone trying to bring a product into the world.
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Every one of them told us they loved it.
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In fact, we had a Porsche driver who came in and told us on the first day,
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"This is completely stupid. What are we thinking?"
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But at the end of it, he said, "Not only should I have it,
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everyone else should have it, because people are terrible drivers."
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So this was music to our ears,
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but then we started to look at what the people inside the car were doing,
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and this was eye-opening.
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Now, my favorite story is this gentleman
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who looks down at his phone and realizes the battery is low,
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so he turns around like this in the car and digs around in his backpack,
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pulls out his laptop,
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puts it on the seat,
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goes in the back again,
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digs around, pulls out the charging cable for his phone,
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futzes around, puts it into the laptop, puts it on the phone.
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Sure enough, the phone is charging.
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All the time he's been doing 65 miles per hour down the freeway.
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Right? Unbelievable.
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So we thought about this and we said, it's kind of obvious, right?
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The better the technology gets,
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the less reliable the driver is going to get.
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So by just making the cars incrementally smarter,
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we're probably not going to see the wins we really need.
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Let me talk about something a little technical for a moment here.
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So we're looking at this graph, and along the bottom
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is how often does the car apply the brakes when it shouldn't.
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You can ignore most of that axis,
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because if you're driving around town, and the car starts stopping randomly,
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you're never going to buy that car.
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And the vertical axis is how often the car is going to apply the brakes
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when it's supposed to to help you avoid an accident.
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Now, if we look at the bottom left corner here,
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this is your classic car.
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It doesn't apply the brakes for you, it doesn't do anything goofy,
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but it also doesn't get you out of an accident.
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Now, if we want to bring a driver assistance system into a car,
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say with collision mitigation braking,
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we're going to put some package of technology on there,
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and that's this curve, and it's going to have some operating properties,
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but it's never going to avoid all of the accidents,
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because it doesn't have that capability.
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But we'll pick some place along the curve here,
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and maybe it avoids half of accidents that the human driver misses,
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and that's amazing, right?
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We just reduced accidents on our roads by a factor of two.
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There are now 17,000 less people dying every year in America.
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But if we want a self-driving car,
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we need a technology curve that looks like this.
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We're going to have to put more sensors in the vehicle,
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and we'll pick some operating point up here
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where it basically never gets into a crash.
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They'll happen, but very low frequency.
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Now you and I could look at this and we could argue
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about whether it's incremental, and I could say something like "80-20 rule,"
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and it's really hard to move up to that new curve.
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But let's look at it from a different direction for a moment.
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So let's look at how often the technology has to do the right thing.
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And so this green dot up here is a driver assistance system.
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It turns out that human drivers
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make mistakes that lead to traffic accidents
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about once every 100,000 miles in America.
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In contrast, a self-driving system is probably making decisions
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about 10 times per second,
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so order of magnitude,
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that's about 1,000 times per mile.
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So if you compare the distance between these two,
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it's about 10 to the eighth, right?
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Eight orders of magnitude.
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That's like comparing how fast I run
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to the speed of light.
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It doesn't matter how hard I train, I'm never actually going to get there.
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So there's a pretty big gap there.
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And then finally, there's how the system can handle uncertainty.
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So this pedestrian here might be stepping into the road, might not be.
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I can't tell, nor can any of our algorithms,
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but in the case of a driver assistance system,
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that means it can't take action, because again,
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if it presses the brakes unexpectedly, that's completely unacceptable.
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Whereas a self-driving system can look at that pedestrian and say,
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I don't know what they're about to do,
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slow down, take a better look, and then react appropriately after that.
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So it can be much safer than a driver assistance system can ever be.
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So that's enough about the differences between the two.
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Let's spend some time talking about how the car sees the world.
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So this is our vehicle.
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It starts by understanding where it is in the world,
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by taking a map and its sensor data and aligning the two,
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and then we layer on top of that what it sees in the moment.
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So here, all the purple boxes you can see are other vehicles on the road,
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and the red thing on the side over there is a cyclist,
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and up in the distance, if you look really closely,
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you can see some cones.
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Then we know where the car is in the moment,
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but we have to do better than that: we have to predict what's going to happen.
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So here the pickup truck in top right is about to make a left lane change
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because the road in front of it is closed,
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so it needs to get out of the way.
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Knowing that one pickup truck is great,
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but we really need to know what everybody's thinking,
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so it becomes quite a complicated problem.
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And then given that, we can figure out how the car should respond in the moment,
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so what trajectory it should follow, how quickly it should slow down or speed up.
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And then that all turns into just following a path:
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turning the steering wheel left or right, pressing the brake or gas.
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It's really just two numbers at the end of the day.
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So how hard can it really be?
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Back when we started in 2009,
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this is what our system looked like.
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So you can see our car in the middle and the other boxes on the road,
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driving down the highway.
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The car needs to understand where it is and roughly where the other vehicles are.
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It's really a geometric understanding of the world.
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Once we started driving on neighborhood and city streets,
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the problem becomes a whole new level of difficulty.
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You see pedestrians crossing in front of us, cars crossing in front of us,
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going every which way,
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the traffic lights, crosswalks.
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It's an incredibly complicated problem by comparison.
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And then once you have that problem solved,
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the vehicle has to be able to deal with construction.
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So here are the cones on the left forcing it to drive to the right,
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but not just construction in isolation, of course.
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It has to deal with other people moving through that construction zone as well.
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And of course, if anyone's breaking the rules, the police are there
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and the car has to understand that that flashing light on the top of the car
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means that it's not just a car, it's actually a police officer.
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Similarly, the orange box on the side here,
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it's a school bus,
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and we have to treat that differently as well.
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When we're out on the road, other people have expectations:
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So, when a cyclist puts up their arm,
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it means they're expecting the car to yield to them and make room for them
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to make a lane change.
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And when a police officer stood in the road,
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our vehicle should understand that this means stop,
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and when they signal to go, we should continue.
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Now, the way we accomplish this is by sharing data between the vehicles.
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The first, most crude model of this
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is when one vehicle sees a construction zone,
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having another know about it so it can be in the correct lane
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to avoid some of the difficulty.
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But we actually have a much deeper understanding of this.
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We could take all of the data that the cars have seen over time,
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the hundreds of thousands of pedestrians, cyclists,
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and vehicles that have been out there
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and understand what they look like
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and use that to infer what other vehicles should look like
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and other pedestrians should look like.
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And then, even more importantly, we could take from that a model
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of how we expect them to move through the world.
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So here the yellow box is a pedestrian crossing in front of us.
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Here the blue box is a cyclist and we anticipate
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that they're going to nudge out and around the car to the right.
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Here there's a cyclist coming down the road
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and we know they're going to continue to drive down the shape of the road.
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Here somebody makes a right turn,
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and in a moment here, somebody's going to make a U-turn in front of us,
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and we can anticipate that behavior and respond safely.
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Now, that's all well and good for things that we've seen,
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but of course, you encounter lots of things that you haven't
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seen in the world before.
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And so just a couple of months ago,
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our vehicles were driving through Mountain View,
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and this is what we encountered.
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This is a woman in an electric wheelchair
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chasing a duck in circles on the road. (Laughter)
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Now it turns out, there is nowhere in the DMV handbook
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that tells you how to deal with that,
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but our vehicles were able to encounter that,
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slow down, and drive safely.
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Now, we don't have to deal with just ducks.
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Watch this bird fly across in front of us. The car reacts to that.
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Here we're dealing with a cyclist
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that you would never expect to see anywhere other than Mountain View.
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And of course, we have to deal with drivers,
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even the very small ones.
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Watch to the right as someone jumps out of this truck at us.
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And now, watch the left as the car with the green box decides
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he needs to make a right turn at the last possible moment.
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Here, as we make a lane change, the car to our left decides
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it wants to as well.
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And here, we watch a car blow through a red light
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and yield to it.
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And similarly, here, a cyclist blowing through that light as well.
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And of course, the vehicle responds safely.
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And of course, we have people who do I don't know what
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sometimes on the road, like this guy pulling out between two self-driving cars.
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You have to ask, "What are you thinking?"
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(Laughter)
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Now, I just fire-hosed you with a lot of stuff there,
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so I'm going to break one of these down pretty quickly.
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So what we're looking at is the scene with the cyclist again,
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and you might notice in the bottom, we can't actually see the cyclist yet,
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but the car can: it's that little blue box up there,
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and that comes from the laser data.
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And that's not actually really easy to understand,
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so what I'm going to do is I'm going to turn that laser data and look at it,
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and if you're really good at looking at laser data, you can see
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a few dots on the curve there,
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right there, and that blue box is that cyclist.
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Now as our light is red,
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the cyclist's light has turned yellow already,
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and if you squint, you can see that in the imagery.
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But the cyclist, we see, is going to proceed through the intersection.
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Our light has now turned green, his is solidly red,
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and we now anticipate that this bike is going to come all the way across.
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Unfortunately the other drivers next to us were not paying as much attention.
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They started to pull forward, and fortunately for everyone,
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this cyclists reacts, avoids,
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and makes it through the intersection.
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And off we go.
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Now, as you can see, we've made some pretty exciting progress,
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and at this point we're pretty convinced
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this technology is going to come to market.
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We do three million miles of testing in our simulators every single day,
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so you can imagine the experience that our vehicles have.
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We are looking forward to having this technology on the road,
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and we think the right path is to go through the self-driving
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rather than driver assistance approach
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because the urgency is so large.
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In the time I have given this talk today,
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34 people have died on America's roads.
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How soon can we bring it out?
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Well, it's hard to say because it's a really complicated problem,
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but these are my two boys.
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My oldest son is 11, and that means in four and a half years,
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he's going to be able to get his driver's license.
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My team and I are committed to making sure that doesn't happen.
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Thank you.
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(Laughter) (Applause)
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Chris Anderson: Chris, I've got a question for you.
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Chris Urmson: Sure.
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CA: So certainly, the mind of your cars is pretty mind-boggling.
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On this debate between driver-assisted and fully driverless --
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I mean, there's a real debate going on out there right now.
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So some of the companies, for example, Tesla,
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are going the driver-assisted route.
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What you're saying is that that's kind of going to be a dead end
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because you can't just keep improving that route and get to fully driverless
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at some point, and then a driver is going to say, "This feels safe,"
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and climb into the back, and something ugly will happen.
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CU: Right. No, that's exactly right, and it's not to say
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that the driver assistance systems aren't going to be incredibly valuable.
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They can save a lot of lives in the interim,
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but to see the transformative opportunity to help someone like Steve get around,
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to really get to the end case in safety,
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to have the opportunity to change our cities
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and move parking out and get rid of these urban craters we call parking lots,
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it's the only way to go.
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CA: We will be tracking your progress with huge interest.
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Thanks so much, Chris. CU: Thank you. (Applause)
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