Chris Urmson: How a driverless car sees the road

翻译人员: Li Li 校对人员: Yuanqing Edberg

So in 1885, Karl Benz invented the automobile.

在1885年，卡尔本茨发明了汽车。

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

那年年末，他把车开出去 进行第一次户外测试,

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

然后——真实是——他撞墙了。

For the last 130 years,

在过去的130年里，

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

我们一直在为汽车的最薄弱环节， 驾驶员，做着努力。

We've made the car stronger.

我们把车做得更坚固,

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

我们增加了安全带，增加了安全气囊。

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

在过去十年里， 我们开始让车变得更智能,

to fix that bug, the driver.

是为弥补驾驶员方面的缺陷。

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

那么，今天我将给大家讲讲，

between patching around the problem with driver assistance systems

用驾驶员辅助系统解决问题，

and actually having fully self-driving cars

和真正使用全面自动驾驶汽车的区别，

and what they can do for the world.

以及它们对世界的贡献。

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

我也会介绍一下我们的车，

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

让你们了解它是如何观察这个世界的， 以及如何应对各种情况，

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

但首先我先说说存在的问题。

And it's a big problem:

这是个很严重的问题，

每年全世界都有120万人 因交通事故丧命。

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

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

仅仅在美国， 每年就有3万3千人死于车祸。

To put that in perspective,

换个方式说,

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

等同于每天都有一架737飞机失事。

It's kind of unbelievable.

有点不可思议。

Cars are sold to us like this,

汽车卖给我们后应该是这样一番景象，

but really, this is what driving's like.

但事实上，驾驶过程通常是这样。

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

对吧？这不是晴天，是雨天，

and you want to do anything other than drive.

而且除了开车， 你还想做点别的事情。

And the reason why is this:

原因就是：

Traffic is getting worse.

交通状况变得越来越糟。

In America, between 1990 and 2010,

在美国，从1990年到2010年,

the vehicle miles traveled increased by 38 percent.

交通工具的里程数增加了38%。

We grew by six percent of roads,

而我们只增修了6%的路,

so it's not in your brains.

所以不单单是你的感觉如此。

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

交通状况的确比以前糟糕得多。

And all of this has a very human cost.

所有的这一切也都伴随着人力成本。

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

如果你把在美的平均通勤时间， 约50分钟，

you multiply that by the 120 million workers we have,

乘以我们的1亿2000万工作者,

that turns out to be about six billion minutes

结果就是60亿分钟，

wasted in commuting every day.

每天会被浪费在路上。

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

这是个很大的数字， 那么我们换个方式,

You take that six billion minutes

你把这60亿分钟，

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

除以人均寿命，

that turns out to be 162 lifetimes

得出数字是162个生命周期。

spent every day, wasted,

仅仅从A地到B地，每天就有这么多

just getting from A to B.

生命白白浪费掉了。

It's unbelievable.

太难以置信了。

And then, there are those of us who don't have the privilege

当然，我们当中还有一些人无法享有

of sitting in traffic.

坐到车流中来的权利。

So this is Steve.

这个人叫史蒂夫。

He's an incredibly capable guy,

他是个很有才能的人。

but he just happens to be blind,

但很不幸，他是盲人，

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

这意味着本来早上 上班路上的30分钟，

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

变成了两个小时的 各种转乘公共交通的折磨，

or asking friends and family for a ride.

或者是请求朋友或家人载他一程。

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

他并没有像你我一样 想去哪儿就去哪儿的自由。

We should do something about that.

我们应该改变这种现状。

Now, conventional wisdom would say

现在，传统观点认为,

that we'll just take these driver assistance systems

我们应该使用驾驶员辅助系统，

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

然后不停改进它们,

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

总有一天，它们能够实现自动驾驶。

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

事实上我今天来就是想告诉你们， 这就跟

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

如果我努力练习弹跳， 有一天我就能飞翔一样不现实。

We actually need to do something a little different.

我们需要做点不同的东西。

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

我会跟你们介绍

自动驾驶系统和驾驶员辅助系统的 三个不同方面。

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

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

我先从自己的经历说起。

So back in 2013,

在2013年，

we had the first test of a self-driving car

我们进行了第一次自动驾驶测试，

where we let regular people use it.

让普通人来操作。

Well, almost regular -- they were 100 Googlers,

算是普通人吧—— 他们是100名谷歌员工，

but they weren't working on the project.

但他们没有参与开发这个项目。

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

我们把车给他们， 让他们在日常生活中使用。

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

但与真的自动驾驶汽车不同， 这一辆得加个星号上去：

They had to pay attention,

他们得留多个心眼儿，

because this was an experimental vehicle.

因为这只是一辆试验车。

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

我们虽然进行了很多测试， 但还是有风险。

And so we gave them two hours of training,

我们对他们进行了两个小时的训练，

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

然后让他们进行实际操作，

and what we heard back was something awesome,

然后我们得到了一些很好的反馈，

as someone trying to bring a product into the world.

因为有人把产品带到现实中来了。

Every one of them told us they loved it.

每个人都对它赞不绝口。

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

事实上，在第一天 有一个保时捷驾驶员进来跟我们说，

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

“这实在是太无厘头了。 你们到底怎么想的？”

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

但最后，他说，“不单是我需要它，

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

每个人都需要有一辆， 大家的车技都太烂了。”

So this was music to our ears,

这番话就是我们的福音，

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

然后我们开始观察 人们在车里都在做什么，

and this was eye-opening.

真让人大开眼界。

Now, my favorite story is this gentleman

我最喜欢的故事，是一位先生

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

低头看手机，发现手机快没电了，

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

然后他在车里这样转过身， 在背包里四处摸索着，

pulls out his laptop,

拿出他的笔记本电脑，

放到副驾驶座位上，

puts it on the seat,

goes in the back again,

再转过身，

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

又摸了一通，拿出手机充电线，

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

理一下线，插进电脑里，连上手机。

Sure enough, the phone is charging.

棒极了，手机有电了。

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

而他那时正行驶在时速65英里的高速上 （约104公里每小时）。

Right? Unbelievable.

能想象到吗？太难以置信了。

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

所以我们想了想，说， 这挺明显的对吧？

The better the technology gets,

科技越来越发达，

the less reliable the driver is going to get.

驾驶员就不需要太负责任。

So by just making the cars incrementally smarter,

所以只是把车变得更加智能，

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

并没法让我们看到真正需要的成功。

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

我在这里要暂时说一点技术上的东西。

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

在这张图上，底部的线

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

代表着在不必要的时候 制动刹车发生的频率。

You can ignore most of that axis,

你可以忽略这条轴的大部分.

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

因为如果你在城里开车， 然后车时不时自己停下来。

you're never going to buy that car.

你永远都不会买这辆车。

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

竖直的轴线表示车会 在你需要避免事故时

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

采取制动刹车的频率。

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

如果我们看左下角,

this is your classic car.

这是你们正在开的普通汽车。

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

它不会自动为你刹车， 也不至于刹车失灵，

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

但它无法为你避免事故。

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

如果我们把驾驶员辅助系统 装进车里，

say with collision mitigation braking,

比如说撞击缓冲刹车系统，

我们会导入一系列的科技，

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,

也就是这条曲线， 它有了一些操作属性，

但也不会完全规避事故，

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

因为它没有这个能力。

because it doesn't have that capability.

但我们会在曲线上取某个点，

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

也许它可以避免一半 因驾驶员失误引起的事故。

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

挺赞的，对吧？

and that's amazing, right?

我们可以减少一半的交通事故。

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

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

这样每年在美国就有1万7千人 幸免于难。

But if we want a self-driving car,

但如果我们想要一辆自动驾驶汽车，

we need a technology curve that looks like this.

我们需要一条这样的曲线。

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

我们得在车里加装更多的传感器，

and we'll pick some operating point up here

然后在这里挑某个操作点，

where it basically never gets into a crash.

在这个点上基本 永远不会有事故发生。

They'll happen, but very low frequency.

多少还是会发生，但概率极低。

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

现在我们可以看看这里，

探讨一下是否有所增加， 我会提到比方说“80-20规则”，

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.

而且很难再上升了。

但让我们暂时从另一个角度看一看。

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

我们看看这种科技应用的频率多高。

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.

这个绿点表示驾驶员辅助系统。

It turns out that human drivers

调查发现人类驾驶员

make mistakes that lead to traffic accidents

因为自身错误导致的交通事故，

about once every 100,000 miles in America.

在美国是每10万英里（约16万公里） 发生一次。

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

对比之下，自动驾驶系统约在每秒

about 10 times per second,

会自行做10次决定。

so order of magnitude,

所以就数量级而言，

that's about 1,000 times per mile.

约是每英里（1.6公里）1000次。

So if you compare the distance between these two,

如果你对比一下两者的差距，

it's about 10 to the eighth, right?

就是10的八次方，对吧？

Eight orders of magnitude.

8个数量级，

That's like comparing how fast I run

这就像拿我跑步的速度

to the speed of light.

和光速比较，

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

即便我再刻苦训练， 也永远达不到光速。

So there's a pretty big gap there.

所以这个跨度非常大。

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

最后，就是这个系统如何 处理不确定性。

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

那么这个行人可能会走到路上， 也可能不会。

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

我不确定，也没有任何算法能确定，

但对于驾驶员辅助系统来说，

but in the case of a driver assistance system,

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

这意味着它无法采取措施，

因为如果它在预期之外采取制动， 是完全不合适的。

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

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

但自动驾驶系统则会观察行人，

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

然后说我不知道他们要做什么，

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

于是减速，再仔细观察， 然后采取适当措施。

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

所以这就比驾驶员辅助系统 要安全得多，

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

那么以上的例子就 足以体现这两者的区别了。

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

现在我们再花点时间聊聊 车是如何观察环境的。

So this is our vehicle.

这是我们的车。

It starts by understanding where it is in the world,

它从识别自己的位置开始，

通过将它的地图和 传感器数据进行叠加，

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.

然后我们再加上它当时看到的东西，

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

所以在这里所有你能看到的紫色盒子 是路上的其他车辆，

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

旁边红色的物体则是一位骑单车的人，

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

如果你再仔细点看，在远处，

you can see some cones.

你能看到一些锥形路障。

Then we know where the car is in the moment,

这样我们就能知道汽车现在的位置了，

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

但我们还得再改善： 我们得预测将发生的事情。

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

这里右上角的小卡车正准备换到左道,

因为前面的路被封了，

because the road in front of it is closed,

所以它得驶离原车道。

so it needs to get out of the way.

Knowing that one pickup truck is great,

能知道一辆小卡车的轨迹确实不错,

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

但我们真正需要的是 了解每个人的想法,

so it becomes quite a complicated problem.

所以问题就变得十分复杂了。

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

了解这些之后， 我们就可以算出汽车该如何应对，

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

该跟随哪条线路， 要多快实现减速或者加速。

And then that all turns into just following a path:

所有的这一切最终都会 变成跟随一条路径：

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

向左或还是右打方向盘， 踩刹车还是油门。

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

所有的一切最终都 简化成了两个数值，

So how hard can it really be?

那这能有多难呢？

Back when we started in 2009,

在2009年我们刚开始测试的时候，

this is what our system looked like.

我们的系统看起来是这样的。

你能看到我们的车在中间， 路上还有其他盒子，

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

同时在高速上行驶着。

driving down the highway.

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

这辆车需要知道自己的位置 以及其他车辆的大概方位。

基本上就是一种几何的分析方式。

It's really a geometric understanding of the world.

Once we started driving on neighborhood and city streets,

当我们开始在社区和 城市道路上行驶时，

the problem becomes a whole new level of difficulty.

问题又上升到了一个新的难度。

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

你能看到行人在我们面前穿梭， 还有汽车,

going every which way,

横纵交错，

the traffic lights, crosswalks.

还有红绿灯，人行横道。

It's an incredibly complicated problem by comparison.

相比之下问题变得极度复杂。

And then once you have that problem solved,

当你把这个问题解决后，

the vehicle has to be able to deal with construction.

接下来汽车还得能应付建筑施工。

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

所以左边的锥形路障会 迫使汽车开到右边，

but not just construction in isolation, of course.

当然不仅需要避开施工区域，

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

它还得应付在其间走动的其他人。

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

当然，如果有人违规了， 有警察在那里，

汽车就得明白车上闪着灯

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.

意味着这不仅仅是一辆车， 还有一位警官。

Similarly, the orange box on the side here,

类似的，这里路边橘黄色的盒子，

是校车，

it's a school bus,

and we have to treat that differently as well.

我们也得分开来处理。

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

当我们在路上时， 其他人会表达各种意图：

So, when a cyclist puts up their arm,

当骑单车的人举起他们的手臂，

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

这就意味着他们希望有车能 让给他们点空间

to make a lane change.

以便进行换道。

And when a police officer stood in the road,

当路中间站着一位警官，

our vehicle should understand that this means stop,

我们的车得明白这手势是要你停下来，

and when they signal to go, we should continue.

当他们示意我们可以走了， 我们才能继续。

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

我们达成这些目标， 是通过和其他车辆分享数据。

The first, most crude model of this

首先，最简单粗制的模型,

is when one vehicle sees a construction zone,

就是当一辆车看到建筑施工地带时,

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

告知另一辆车，让它驶上正确的车道

to avoid some of the difficulty.

以避免麻烦。

But we actually have a much deeper understanding of this.

但我们对此有更深的认识。

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

我们可以搜集车辆在一段时间内 看到的数据，

the hundreds of thousands of pedestrians, cyclists,

数以千计在路上的行人，骑单车的人，

and vehicles that have been out there

以及其他车辆，

and understand what they look like

了解他们的外形，

and use that to infer what other vehicles should look like

再用之去推理其他车辆

以及其他行人的外形。

and other pedestrians should look like.

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

然后，更重要的是， 我们可以从中得出一个模型,

of how we expect them to move through the world.

预测所有交通参与者的去向。

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

这里的黄色盒子是 我们面前过马路的行人。

Here the blue box is a cyclist and we anticipate

这个蓝色盒子是个骑单车的人， 而且我们预测

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

他会一直保持在车辆右侧。

Here there's a cyclist coming down the road

这里有个在路上骑单车的人，

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

我们知道他会沿着路一直骑下去。

这里有人右转了,

Here somebody makes a right turn,

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

而在这里，有人会在我们面前调头，

and we can anticipate that behavior and respond safely.

我们可以预测这个行为并安全应对。

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

目前为止，对于我们见过的场景 都没什么问题，

但当然，你还会遇见很多

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

之前没见过的东西。

seen in the world before.

几个月前，

And so just a couple of months ago,

我们的车辆在通过Mountain View （硅谷地名）的时候，

our vehicles were driving through Mountain View,

就遇到了这样的情景。

and this is what we encountered.

这是个坐着电动轮椅的女士,

This is a woman in an electric wheelchair

在路中央绕着圈追赶一只鸭子。 （笑声）

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

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

结果呢，在机动车驾驶管理处的手册里

that tells you how to deal with that,

没有一条告诉你该怎么做，

but our vehicles were able to encounter that,

但我们的车辆却能灵活应对，

减速并安全通过。

slow down, and drive safely.

Now, we don't have to deal with just ducks.

我们应付的不只是鸭子,

Watch this bird fly across in front of us. The car reacts to that.

看看这只飞过我们面前的鸟， 汽车也会对之做出处理。

Here we're dealing with a cyclist

这里还有一个骑车的,

that you would never expect to see anywhere other than Mountain View.

估计除了在Mountain View， 其他地方很难见到。

And of course, we have to deal with drivers,

当然，我们还得应付其他驾驶员，

even the very small ones.

甚至那些幼龄的。

Watch to the right as someone jumps out of this truck at us.

注意右边，那个从货车上跳下来的人。

And now, watch the left as the car with the green box decides

现在，注意左边绿盒子那辆车，

he needs to make a right turn at the last possible moment.

它决定在最后的时刻右转。

Here, as we make a lane change, the car to our left decides

这里，当我们变道时，我们左边的车

it wants to as well.

也同样想变道。

And here, we watch a car blow through a red light

还有这里，我们看到一辆车闯了红灯，

and yield to it.

我们就先让它通过。

And similarly, here, a cyclist blowing through that light as well.

同样这里，骑单车的人也闯红灯了，

And of course, the vehicle responds safely.

不出所料， 我们的车也能安全应对。

And of course, we have people who do I don't know what

当然还有一些莫名其妙的人，

sometimes on the road, like this guy pulling out between two self-driving cars.

就像这家伙一样， 直接就从两辆自动驾驶汽车中窜出来。

You have to ask, "What are you thinking?"

你会想问，“你脑子是怎么想的？”

(Laughter)

（笑声）

Now, I just fire-hosed you with a lot of stuff there,

我已经给你们看了大量的例子，

so I'm going to break one of these down pretty quickly.

我快速地讲一下其中的一个案例。

我们现在看到的还是骑单车的人，

So what we're looking at is the scene with the cyclist again,

你们可能会发现在下面的视角， 我们还看不到那个人，

and you might notice in the bottom, we can't actually see the cyclist yet,

but the car can: it's that little blue box up there,

但车能看到：就是那里的小蓝盒子,

这来自于激光数据。

and that comes from the laser data.

这并不是很容易理解，

And that's not actually really easy to understand,

那么我接下来要做的， 就是调出激光数据看一下,

so what I'm going to do is I'm going to turn that laser data and look at it,

如果你擅长分析激光数据，你能发现

and if you're really good at looking at laser data, you can see

曲线上的一些点，

就在那儿， 那个蓝色小盒子就是骑单车的人。

a few dots on the curve there,

right there, and that blue box is that cyclist.

这会儿面对我们的还是红灯，

Now as our light is red,

自行车道的灯已经变黄了，

the cyclist's light has turned yellow already,

如果你瞥一眼的话，就能看到了。

and if you squint, you can see that in the imagery.

但是骑单车的人， 我们看到他准备穿过这个十字路口，

But the cyclist, we see, is going to proceed through the intersection.

Our light has now turned green, his is solidly red,

我们的灯已经变绿， 他的方向也变红了，

and we now anticipate that this bike is going to come all the way across.

我们预期到这辆单车将会横穿马路。

Unfortunately the other drivers next to us were not paying as much attention.

但不幸的是， 我们旁边的其他司机并没有注意到。

They started to pull forward, and fortunately for everyone,

他们开始踩油门, 不过幸运的是，

this cyclists reacts, avoids,

骑单车的人及时避开了，

and makes it through the intersection.

然后平安地穿过了十字路口。

And off we go.

之后我们才又继续前进。

Now, as you can see, we've made some pretty exciting progress,

正如你们所见， 我们已取得了一些激动人心的成就，

and at this point we're pretty convinced

此时我们深信

this technology is going to come to market.

这项技术将会进入市场。

We do three million miles of testing in our simulators every single day,

我们每天用虚拟器做 3百万英里的测试，

so you can imagine the experience that our vehicles have.

所以你就能够想象到 我们的车辆获得了多少经验。

We are looking forward to having this technology on the road,

我们期待这项技术能在道路上使用，

and we think the right path is to go through the self-driving

并且认为正确的选择是自动驾驶，

rather than driver assistance approach

而非驾驶员辅助系统，

because the urgency is so large.

因为情况已经刻不容缓了。

In the time I have given this talk today,

就在我演讲的时间段内,

34 people have died on America's roads.

在美国的公路上已经有34人丧生。

How soon can we bring it out?

我们多久能实现这个目标呢？

Well, it's hard to say because it's a really complicated problem,

嗯，很难说， 因为这是个很复杂的问题，

but these are my two boys.

这两个是我的儿子。

My oldest son is 11, and that means in four and a half years,

大的11岁，也就是说在四年半后，

he's going to be able to get his driver's license.

他就能去考驾照了。

My team and I are committed to making sure that doesn't happen.

我和我的团队承诺 尽量不让他去考（已经不需要了）。

Thank you.

谢谢。

(Laughter) (Applause)

（笑声）（掌声）

克利斯·安德森（CA）： 克里斯，我有个问题要问你。

Chris Anderson: Chris, I've got a question for you.

Chris Urmson: Sure.

克里斯·厄姆森（CU）：问吧。

CA: So certainly, the mind of your cars is pretty mind-boggling.

CA：显而易见， 你们的车有着让人惊奇的大脑。

On this debate between driver-assisted and fully driverless --

在驾驶辅助和无人驾驶这场辩论上——

I mean, there's a real debate going on out there right now.

我是说，现在就有一场真正的辩论。

So some of the companies, for example, Tesla,

一些公司，例如，特斯拉，

are going the driver-assisted route.

正在走驾驶辅助的路线。

What you're saying is that that's kind of going to be a dead end

而你所说的， 就是这将是个没前途的死胡同,

because you can't just keep improving that route and get to fully driverless

因为你不能指望在这方面不断提高 就会在某个时候实现无人驾驶，

at some point, and then a driver is going to say, "This feels safe,"

然后有驾驶员说 “这已经挺安全的了”，

and climb into the back, and something ugly will happen.

然后转身去后座， 不幸就发生了。

CU: Right. No, that's exactly right, and it's not to say

CU：对，你说得对，这并不是说

that the driver assistance systems aren't going to be incredibly valuable.

驾驶员辅助系统作用不大。

They can save a lot of lives in the interim,

它能在这个过渡阶段拯救很多生命,

但为了抓住这一变革性的机会， 能帮助像史蒂夫这样的人行动自如，

but to see the transformative opportunity to help someone like Steve get around,

to really get to the end case in safety,

为了终结安全事故，

to have the opportunity to change our cities

为了有机会改变我们的城市，

and move parking out and get rid of these urban craters we call parking lots,

解决停车问题， 摆脱我们称为停车场的城市大坑,

it's the only way to go.

这是唯一的办法了。

CA: We will be tracking your progress with huge interest.

CA：我们会带着浓厚的兴趣 关注你们的进展的。

Thanks so much, Chris. CU: Thank you. (Applause)

谢谢你，克里斯。 CU：谢谢。（掌声）