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  • So in 1885, Karl Benz invented the automobile.

  • Later that year, he took it out for the first public test drive,

  • and -- true story -- crashed into a wall.

  • For the last 130 years,

  • we've been working around that least reliable part of the car, the driver.

  • We've made the car stronger.

  • We've added seat belts, we've added air bags,

  • and in the last decade, we've actually started trying to make the car smarter

  • to fix that bug, the driver.

  • Now, today I'm going to talk to you a little bit about the difference

  • between patching around the problem with driver assistance systems

  • and actually having fully self-driving cars

  • and what they can do for the world.

  • I'm also going to talk to you a little bit about our car

  • and allow you to see how it sees the world and how it reacts and what it does,

  • but first I'm going to talk a little bit about the problem.

  • And it's a big problem:

  • 1.2 million people are killed on the world's roads every year.

  • In America alone, 33,000 people are killed each year.

  • To put that in perspective,

  • that's the same as a 737 falling out of the sky every working day.

  • It's kind of unbelievable.

  • Cars are sold to us like this,

  • but really, this is what driving's like.

  • Right? It's not sunny, it's rainy,

  • and you want to do anything other than drive.

  • And the reason why is this:

  • Traffic is getting worse.

  • In America, between 1990 and 2010,

  • the vehicle miles traveled increased by 38 percent.

  • We grew by six percent of roads,

  • so it's not in your brains.

  • Traffic really is substantially worse than it was not very long ago.

  • And all of this has a very human cost.

  • So if you take the average commute time in America, which is about 50 minutes,

  • you multiply that by the 120 million workers we have,

  • that turns out to be about six billion minutes

  • wasted in commuting every day.

  • Now, that's a big number, so let's put it in perspective.

  • You take that six billion minutes

  • and you divide it by the average life expectancy of a person,

  • that turns out to be 162 lifetimes

  • spent every day, wasted,

  • just getting from A to B.

  • It's unbelievable.

  • And then, there are those of us who don't have the privilege of sitting in traffic.

  • So this is Steve.

  • He's an incredibly capable guy,

  • but he just happens to be blind,

  • and that means instead of a 30-minute drive to work in the morning,

  • it's a two-hour ordeal of piecing together bits of public transit

  • or asking friends and family for a ride.

  • He doesn't have that same freedom that you and I have to get around.

  • We should do something about that.

  • Now, conventional wisdom would say

  • that we'll just take these driver assistance systems

  • and we'll kind of push them and incrementally improve them,

  • and over time, they'll turn into self-driving cars.

  • Well, I'm here to tell you that's like me saying

  • that if I work really hard at jumping, one day I'll be able to fly.

  • We actually need to do something a little different.

  • And so I'm going to talk to you about three different ways

  • that self-driving systems are different than driver assistance systems.

  • And I'm going to start with some of our own experience.

  • So back in 2013,

  • we had the first test of a self-driving car

  • where we let regular people use it.

  • Well, almost regular -- they were 100 Googlers,

  • but they weren't working on the project.

  • And we gave them the car and we allowed them to use it in their daily lives.

  • But unlike a real self-driving car, this one had a big asterisk with it:

  • They had to pay attention,

  • because this was an experimental vehicle.

  • We tested it a lot, but it could still fail.

  • And so we gave them two hours of training,

  • we put them in the car, we let them use it,

  • and what we heard back was something awesome,

  • as someone trying to bring a product into the world.

  • Every one of them told us they loved it.

  • In fact, we had a Porsche driver who came in and told us on the first day,

  • "This is completely stupid. What are we thinking?"

  • But at the end of it, he said, "Not only should I have it,

  • everyone else should have it, because people are terrible drivers."

  • So this was music to our ears,

  • but then we started to look at what the people inside the car were doing,

  • and this was eye-opening.

  • Now, my favorite story is this gentleman

  • who looks down at his phone and realizes the battery is low,

  • so he turns around like this in the car and digs around in his backpack,

  • pulls out his laptop,

  • puts it on the seat,

  • goes in the back again,

  • digs around, pulls out the charging cable for his phone,

  • futzes around, puts it into the laptop, puts it on the phone.

  • Sure enough, the phone is charging.

  • All the time he's been doing 65 miles per hour down the freeway.

  • Right? Unbelievable.

  • So we thought about this and we said, it's kind of obvious, right?

  • The better the technology gets,

  • the less reliable the driver is going to get.

  • So by just making the cars incrementally smarter,

  • we're probably not going to see the wins we really need.

  • Let me talk about something a little technical for a moment here.

  • So we're looking at this graph, and along the bottom

  • is how often does the car apply the brakes when it shouldn't.

  • You can ignore most of that axis,

  • because if you're driving around town, and the car starts stopping randomly,

  • you're never going to buy that car.

  • And the vertical axis is how often the car is going to apply the brakes

  • when it's supposed to to help you avoid an accident.

  • Now, if we look at the bottom left corner here,

  • this is your classic car.

  • It doesn't apply the brakes for you, it doesn't do anything goofy,

  • but it also doesn't get you out of an accident.

  • Now, if we want to bring a driver assistance system into a car,

  • say with collision mitigation braking,

  • we're going to put some package of technology on there, and that's this curve,

  • and it's going to have some operating properties,

  • but it's never going to avoid all of the accidents,

  • because it doesn't have that capability.

  • But we'll pick some place along the curve here,

  • and maybe it avoids half of accidents that the human driver misses,

  • and that's amazing, right?

  • We just reduced accidents on our roads by a factor of two.

  • There are now 17,000 less people dying every year in America.

  • But if we want a self-driving car,

  • we need a technology curve that looks like this.

  • We're going to have to put more sensors in the vehicle,

  • and we'll pick some operating point up here

  • where it basically never gets into a crash.

  • They'll happen, but very low frequency.

  • Now you and I could look at this and we could argue

  • about whether it's incremental, and I could say something like "80-20 rule,"

  • and it's really hard to move up to that new curve.

  • But let's look at it from a different direction for a moment.

  • So let's look at how often the technology has to do the right thing.

  • And so this green dot up here is a driver assistance system.

  • It turns out that human drivers

  • make mistakes that lead to traffic accidents

  • about once every 100,000 miles in America.

  • In contrast, a self-driving system is probably making decisions

  • about 10 times per second,

  • so order of magnitude,

  • that's about 1,000 times per mile.

  • So if you compare the distance between these two,

  • it's about 10 to the eighth, right?

  • Eight orders of magnitude.

  • That's like comparing how fast I run

  • to the speed of light.

  • It doesn't matter how hard I train, I'm never actually going to get there.

  • So there's a pretty big gap there.

  • And then finally, there's how the system can handle uncertainty.

  • So this pedestrian here might be stepping into the road, might not be.

  • I can't tell, nor can any of our algorithms,

  • but in the case of a driver assistance system,

  • that means it can't take action, because again,

  • if it presses the brakes unexpectedly, that's completely unacceptable.

  • Whereas a self-driving system can look at that pedestrian and say,

  • I don't know what they're about to do,

  • slow down, take a better look, and then react appropriately after that.

  • So it can be much safer than a driver assistance system can ever be.

  • So that's enough about the differences between the two.

  • Let's spend some time talking about how the car sees the world.

  • So this is our vehicle.

  • It starts by understanding where it is in the world,

  • by taking a map and its sensor data and aligning the two,

  • and then we layer on top of that what it sees in the moment.

  • So here, all the purple boxes you can see are other vehicles on the road,

  • and the red thing on the side over there is a cyclist,

  • and up in the distance, if you look really closely,

  • you can see some cones.

  • Then we know where the car is in the moment,

  • but we have to do better than that: we have to predict what's going to happen.

  • So here the pickup truck in top right is about to make a left lane change

  • because the road in front of it is closed,

  • so it needs to get out of the way.

  • Knowing that one pickup truck is great,

  • but we really need to know what everybody's thinking,

  • so it becomes quite a complicated problem.

  • And then given that, we can figure out how the car should respond in the moment,

  • so what trajectory it should follow, how quickly it should slow down or speed up.

  • And then that all turns into just following a path:

  • turning the steering wheel left or right, pressing the brake or gas.

  • It's really just two numbers at the end of the day.

  • So how hard can it really be?

  • Back when we started in 2009,

  • this is what our system looked like.

  • So you can see our car in the middle and the other boxes on the road,

  • driving down the highway.

  • The car needs to understand where it is and roughly where the other vehicles are.

  • It's really a geometric understanding of the world.

  • Once we started driving on neighborhood and city streets,

  • the problem becomes a whole new level of difficulty.

  • You see pedestrians crossing in front of us, cars crossing in front of us,

  • going every which way,

  • the traffic lights, crosswalks.

  • It's an incredibly complicated problem by comparison.

  • And then once you have that problem solved,

  • the vehicle has to be able to deal with construction.

  • So here are the cones on the left forcing it to drive to the right,

  • but not just construction in isolation, of course.

  • It has to deal with other people moving through that construction zone as well.

  • And of course, if anyone's breaking the rules, the police are there

  • and the car has to understand that that flashing light on the top of the car

  • means that it's not just a car, it's actually a police officer.

  • Similarly, the orange box on the side here,

  • it's a school bus,

  • and we have to treat that differently as well.

  • When we're out on the road, other people have expectations:

  • So, when a cyclist puts up their arm,

  • it means they're expecting the car to yield to them and make room for them

  • to make a lane change.

  • And when a police officer stood in the road,

  • our vehicle should understand that this means stop,

  • and when they signal to go, we should continue.

  • Now, the way we accomplish this is by sharing data between the vehicles.

  • The first, most crude model of this

  • is when one vehicle sees a construction zone,

  • having another know about it so it can be in the correct lane

  • to avoid some of the difficulty.

  • But we actually have a much deeper understanding of this.

  • We could take all of the data that the cars have seen over time,

  • the hundreds of thousands of pedestrians, cyclists,

  • and vehicles that have been out there

  • and understand what they look like

  • and use that to infer what other vehicles should look like

  • and other pedestrians should look like.

  • And then, even more importantly, we could take from that a model

  • of how we expect them to move through the world.

  • So here the yellow box is a pedestrian crossing in front of us.

  • Here the blue box is a cyclist and we anticipate

  • that they're going to nudge out and around the car to the right.

  • Here there's a cyclist coming down the road

  • and we know they're going to continue to drive down the shape of the road.

  • Here somebody makes a right turn,

  • and in a moment here, somebody's going to make a U-turn in front of us,

  • and we can anticipate that behavior and respond safely.

  • Now, that's all well and good for things that we've seen,

  • but of course, you encounter lots of things that you haven't

  • seen in the world before.

  • And so just a couple of months ago,

  • our vehicles were driving through Mountain View,

  • and this is what we encountered.

  • This is a woman in an electric wheelchair

  • chasing a duck in circles on the road. (Laughter)

  • Now it turns out, there is nowhere in the DMV handbook