Chris Urmson: How a driverless car sees the road
克里斯·厄姆森: 无人驾驶汽车是如何看清路况的
Double-click the English transcript below to play the video.
invented the automobile.
for the first public test drive,
进行第一次户外测试,
crashed into a wall.
reliable part of the car, the driver.
驾驶员,做着努力。
we've added air bags,
started trying to make the car smarter
我们开始让车变得更智能,
a little bit about the difference
with driver assistance systems
self-driving cars
a little bit about our car
and how it reacts and what it does,
以及如何应对各种情况,
a little bit about the problem.
因交通事故丧命。
on the world's roads every year.
are killed each year.
每年就有3万3千人死于车祸。
falling out of the sky every working day.
other than drive.
你还想做点别的事情。
increased by 38 percent.
than it was not very long ago.
in America, which is about 50 minutes,
约50分钟,
workers we have,
about six billion minutes
so let's put it in perspective.
那么我们换个方式,
life expectancy of a person,
who don't have the privilege
drive to work in the morning,
上班路上的30分钟,
of piecing together bits of public transit
各种转乘公共交通的折磨,
that you and I have to get around.
想去哪儿就去哪儿的自由。
these driver assistance systems
and incrementally improve them,
into self-driving cars.
that's like me saying
这就跟
one day I'll be able to fly.
有一天我就能飞翔一样不现实。
something a little different.
about three different ways
三个不同方面。
than driver assistance systems.
with some of our own experience.
of a self-driving car
they were 100 Googlers,
他们是100名谷歌员工,
them to use it in their daily lives.
让他们在日常生活中使用。
this one had a big asterisk with it:
这一辆得加个星号上去:
but it could still fail.
但还是有风险。
we let them use it,
was something awesome,
to bring a product into the world.
who came in and told us on the first day,
有一个保时捷驾驶员进来跟我们说,
What are we thinking?"
你们到底怎么想的?”
"Not only should I have it,
大家的车技都太烂了。”
because people are terrible drivers."
the people inside the car were doing,
人们在车里都在做什么,
and realizes the battery is low,
and digs around in his backpack,
在背包里四处摸索着,
the charging cable for his phone,
puts it on the phone.
65 miles per hour down the freeway.
(约104公里每小时)。
it's kind of obvious, right?
这挺明显的对吧?
the driver is going to get.
incrementally smarter,
the wins we really need.
a little technical for a moment here.
and along the bottom
apply the brakes when it shouldn't.
制动刹车发生的频率。
and the car starts stopping randomly,
然后车时不时自己停下来。
the car is going to apply the brakes
在你需要避免事故时
to help you avoid an accident.
the bottom left corner here,
it doesn't do anything goofy,
也不至于刹车失灵,
out of an accident.
a driver assistance system into a car,
装进车里,
of technology on there,
它有了一些操作属性,
to have some operating properties,
all of the accidents,
along the curve here,
因驾驶员失误引起的事故。
that the human driver misses,
by a factor of two.
dying every year in America.
幸免于难。
that looks like this.
more sensors in the vehicle,
operating point up here
永远不会有事故发生。
gets into a crash.
and we could argue
我会提到比方说“80-20规则”,
I could say something like "80-20 rule,"
to that new curve.
from a different direction for a moment.
the technology has to do the right thing.
is a driver assistance system.
to traffic accidents
发生一次。
is probably making decisions
between these two,
I'm never actually going to get there.
也永远达不到光速。
the system can handle uncertainty.
处理不确定性。
stepping into the road, might not be.
也可能不会。
nor can any of our algorithms,
a driver assistance system,
because again,
是完全不合适的。
that's completely unacceptable.
can look at that pedestrian and say,
and then react appropriately after that.
然后采取适当措施。
a driver assistance system can ever be.
要安全得多,
the differences between the two.
足以体现这两者的区别了。
how the car sees the world.
车是如何观察环境的。
where it is in the world,
传感器数据进行叠加,
and aligning the two,
what it sees in the moment.
are other vehicles on the road,
是路上的其他车辆,
over there is a cyclist,
if you look really closely,
is in the moment,
we have to predict what's going to happen.
我们得预测将发生的事情。
is about to make a left lane change
what everybody's thinking,
了解每个人的想法,
how the car should respond in the moment,
我们就可以算出汽车该如何应对,
要多快实现减速或者加速。
quickly it should slow down or speed up.
just following a path:
变成跟随一条路径:
pressing the brake or gas.
踩刹车还是油门。
at the end of the day.
简化成了两个数值,
路上还有其他盒子,
and the other boxes on the road,
以及其他车辆的大概方位。
and roughly where the other vehicles are.
understanding of the world.
on neighborhood and city streets,
城市道路上行驶时,
new level of difficulty.
of us, cars crossing in front of us,
还有汽车,
problem by comparison.
that problem solved,
to deal with construction.
forcing it to drive to the right,
迫使汽车开到右边,
in isolation, of course.
through that construction zone as well.
breaking the rules, the police are there
有警察在那里,
that flashing light on the top of the car
还有一位警官。
it's actually a police officer.
on the side here,
differently as well.
other people have expectations:
其他人会表达各种意图:
to yield to them and make room for them
让给他们点空间
stood in the road,
that this means stop,
we should continue.
我们才能继续。
is by sharing data between the vehicles.
是通过和其他车辆分享数据。
sees a construction zone,
so it can be in the correct lane
deeper understanding of this.
that the cars have seen over time,
看到的数据,
of pedestrians, cyclists,
what other vehicles should look like
we could take from that a model
我们可以从中得出一个模型,
to move through the world.
crossing in front of us.
我们面前过马路的行人。
and we anticipate
而且我们预测
and around the car to the right.
coming down the road
to drive down the shape of the road.
going to make a U-turn in front of us,
and respond safely.
都没什么问题,
for things that we've seen,
lots of things that you haven't
(硅谷地名)的时候,
through Mountain View,
(笑声)
(Laughter)
in the DMV handbook
to encounter that,
with just ducks.
汽车也会对之做出处理。
The car reacts to that.
anywhere other than Mountain View.
其他地方很难见到。
to deal with drivers,
jumps out of this truck at us.
with the green box decides
at the last possible moment.
the car to our left decides
blow through a red light
blowing through that light as well.
the vehicle responds safely.
我们的车也能安全应对。
who do I don't know what
pulling out between two self-driving cars.
直接就从两辆自动驾驶汽车中窜出来。
with a lot of stuff there,
down pretty quickly.
with the cyclist again,
我们还看不到那个人,
we can't actually see the cyclist yet,
blue box up there,
really easy to understand,
就是调出激光数据看一下,
to turn that laser data and look at it,
at laser data, you can see
那个蓝色小盒子就是骑单车的人。
is that cyclist.
has turned yellow already,
in the imagery.
我们看到他准备穿过这个十字路口,
to proceed through the intersection.
他的方向也变红了,
his is solidly red,
is going to come all the way across.
were not paying as much attention.
我们旁边的其他司机并没有注意到。
and fortunately for everyone,
不过幸运的是,
some pretty exciting progress,
我们已取得了一些激动人心的成就,
to come to market.
in our simulators every single day,
3百万英里的测试,
that our vehicles have.
我们的车辆获得了多少经验。
this technology on the road,
is to go through the self-driving
it's a really complicated problem,
因为这是个很复杂的问题,
in four and a half years,
to get his driver's license.
to making sure that doesn't happen.
尽量不让他去考(已经不需要了)。
克里斯,我有个问题要问你。
I've got a question for you.
is pretty mind-boggling.
你们的车有着让人惊奇的大脑。
driver-assisted and fully driverless --
going on out there right now.
for example, Tesla,
that's kind of going to be a dead end
就是这将是个没前途的死胡同,
that route and get to fully driverless
就会在某个时候实现无人驾驶,
is going to say, "This feels safe,"
“这已经挺安全的了”,
and something ugly will happen.
不幸就发生了。
and it's not to say
aren't going to be incredibly valuable.
in the interim,
能帮助像史蒂夫这样的人行动自如,
to help someone like Steve get around,
to change our cities
these urban craters we call parking lots,
摆脱我们称为停车场的城市大坑,
with huge interest.
关注你们的进展的。
CU: Thank you. (Applause)
CU:谢谢。(掌声)
ABOUT THE SPEAKER
Chris Urmson - RoboticistChris Umson is the Director of Self-Driving Cars at Google[x].
Why you should listen
Since 2009, Chris Urmson has headed up Google’s self-driving car program. So far, the team’s vehicles have driven over three quarters of a million miles. While early models included a driverless Prius that TEDsters got to test- ... um, -not-drive in 2011, more and more the team is building vehicles from the ground up, custom-made to go driverless.
Prior to joining Google, Umson was on the faculty of the Robotics Institute at Carnegie Mellon University, where his research focused on motion planning and perception for robotic vehicles. During his time at Carnegie Mellon, he served as Director of Technology for the team that won the 2007 DARPA Urban Challenge.
Chris Urmson | Speaker | TED.com