WEBVTT

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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?

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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.

NOTE Paragraph

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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,

00:11:33.153 --> 00:11:36.868
even the very small ones.

00:11:36.868 --> 00:11:40.999
Watch to the right as someone
jumps out of this truck at us.

00:11:42.460 --> 00:11:45.389
And now, watch the left as the car
with the green box decides

00:11:45.389 --> 00:11:48.714
he needs to make a right turn
at the last possible moment.

00:11:48.714 --> 00:11:51.565
Here, as we make a lane change,
the car to our left decides

00:11:51.565 --> 00:11:55.118
it wants to as well.

00:11:55.118 --> 00:11:57.811
And here, we watch a car
blow through a red light

00:11:57.811 --> 00:11:59.901
and yield to it.

00:11:59.901 --> 00:12:03.755
And similarly, here, a cyclist
blowing through that light as well.

00:12:03.755 --> 00:12:06.501
And of course,
the vehicle responds safely.

00:12:06.501 --> 00:12:09.102
And of course, we have people
who do I don't know what

00:12:09.102 --> 00:12:12.925
sometimes on the road, like this guy
pulling out between two self-driving cars.

00:12:12.925 --> 00:12:14.970
You have to ask, "What are you thinking?"

00:12:14.970 --> 00:12:16.182
(Laughter)

NOTE Paragraph

00:12:16.182 --> 00:12:18.703
Now, I just fire-hosed you
with a lot of stuff there,

00:12:18.703 --> 00:12:21.353
so I'm going to break one of these
down pretty quickly.

00:12:21.353 --> 00:12:24.293
So what we're looking at is the scene
with the cyclist again,

00:12:24.293 --> 00:12:27.784
and you might notice in the bottom,
we can't actually see the cyclist yet,

00:12:27.784 --> 00:12:30.288
but the car can: it's that little
blue box up there,

00:12:30.288 --> 00:12:32.369
and that comes from the laser data.

00:12:32.369 --> 00:12:34.787
And that's not actually
really easy to understand,

00:12:34.787 --> 00:12:38.371
so what I'm going to do is I'm going
to turn that laser data and look at it,

00:12:38.371 --> 00:12:41.400
and if you're really good at looking
at laser data, you can see

00:12:41.400 --> 00:12:42.887
a few dots on the curve there,

00:12:42.887 --> 00:12:45.259
right there, and that blue box
is that cyclist.

00:12:45.259 --> 00:12:46.408
Now as our light is red,

00:12:46.408 --> 00:12:48.600
the cyclist's light
has turned yellow already,

00:12:48.600 --> 00:12:51.038
and if you squint, you can see that
in the imagery.

00:12:51.038 --> 00:12:54.324
But the cyclist, we see, is going
to proceed through the intersection.

00:12:54.324 --> 00:12:56.718
Our light has now turned green,
his is solidly red,

00:12:56.718 --> 00:13:01.010
and we now anticipate that this bike
is going to come all the way across.

00:13:01.010 --> 00:13:04.752
Unfortunately the other drivers next to us
were not paying as much attention.

00:13:04.752 --> 00:13:07.909
They started to pull forward,
and fortunately for everyone,

00:13:07.909 --> 00:13:10.920
this cyclists reacts, avoids,

00:13:10.920 --> 00:13:13.111
and makes it through the intersection.

00:13:13.111 --> 00:13:14.679
And off we go.

NOTE Paragraph

00:13:14.679 --> 00:13:17.627
Now, as you can see, we've made
some pretty exciting progress,

00:13:17.627 --> 00:13:19.529
and at this point we're pretty convinced

00:13:19.529 --> 00:13:21.539
this technology is going
to come to market.

00:13:21.539 --> 00:13:26.322
We do three million miles of testing
in our simulators every single day,

00:13:26.322 --> 00:13:29.011
so you can imagine the experience
that our vehicles have.

00:13:29.011 --> 00:13:31.875
We are looking forward to having
this technology on the road,

00:13:31.875 --> 00:13:34.765
and we think the right path
is to go through the self-driving

00:13:34.765 --> 00:13:36.609
rather than driver assistance approach

00:13:36.609 --> 00:13:39.230
because the urgency is so large.

00:13:39.230 --> 00:13:41.623
In the time I have given this talk today,

00:13:41.623 --> 00:13:44.758
34 people have died on America's roads.

NOTE Paragraph

00:13:44.758 --> 00:13:47.126
How soon can we bring it out?

00:13:47.126 --> 00:13:50.958
Well, it's hard to say because
it's a really complicated problem,

00:13:50.958 --> 00:13:53.172
but these are my two boys.

00:13:53.172 --> 00:13:56.795
My oldest son is 11, and that means
in four and a half years,

00:13:56.795 --> 00:13:59.372
he's going to be able
to get his driver's license.

00:13:59.372 --> 00:14:02.576
My team and I are committed
to making sure that doesn't happen.

NOTE Paragraph

00:14:02.576 --> 00:14:04.480
Thank you.

NOTE Paragraph

00:14:04.480 --> 00:14:08.147
(Laughter) (Applause)

00:14:09.110 --> 00:14:11.678
Chris Anderson: Chris,
I've got a question for you.

NOTE Paragraph

00:14:11.678 --> 00:14:14.487
Chris Urmson: Sure.

NOTE Paragraph

00:14:14.487 --> 00:14:18.411
CA: So certainly, the mind of your cars
is pretty mind-boggling.

00:14:18.411 --> 00:14:22.870
On this debate between
driver-assisted and fully driverless --

00:14:22.870 --> 00:14:25.911
I mean, there's a real debate
going on out there right now.

00:14:25.911 --> 00:14:28.744
So some of the companies,
for example, Tesla,

00:14:28.744 --> 00:14:30.903
are going the driver-assisted route.

00:14:30.903 --> 00:14:36.151
What you're saying is that
that's kind of going to be a dead end

00:14:36.151 --> 00:14:41.607
because you can't just keep improving
that route and get to fully driverless

00:14:41.607 --> 00:14:45.137
at some point, and then a driver
is going to say, "This feels safe,"

00:14:45.137 --> 00:14:47.784
and climb into the back,
and something ugly will happen.

NOTE Paragraph

00:14:47.784 --> 00:14:50.460
CU: Right. No, that's exactly right,
and it's not to say

00:14:50.460 --> 00:14:53.997
that the driver assistance systems
aren't going to be incredibly valuable.

00:14:53.997 --> 00:14:56.055
They can save a lot of lives
in the interim,

00:14:56.055 --> 00:14:59.888
but to see the transformative opportunity
to help someone like Steve get around,

00:14:59.888 --> 00:15:01.857
to really get to the end case in safety,

00:15:01.857 --> 00:15:04.336
to have the opportunity
to change our cities

00:15:04.336 --> 00:15:08.540
and move parking out and get rid of
these urban craters we call parking lots,

00:15:08.540 --> 00:15:09.780
it's the only way to go.

NOTE Paragraph

00:15:09.780 --> 00:15:12.498
CA: We will be tracking your progress
with huge interest.

00:15:12.498 --> 00:15:16.730
Thanks so much, Chris.
CU: Thank you. (Applause)