听力课堂TED音频栏目主要包括TED演讲的音频MP3及中英双语文稿，供各位英语爱好者学习使用。本文主要内容为演讲MP3+双语文稿:机器人出租车时代要来了？，希望你会喜欢！

【主讲人】Aicha Evans
无人驾驶汽车公司Zoox首席执行官，前英特尔公司的资深高管。

【演讲主题】Your self-driving robotaxi is almost here
大家一定对自动驾驶汽车不陌生，演讲者向我们介绍了机器人出租车：它是完全自主、环保的，可以把你从一个地方带到另一个地方，而且比私家车占用的街道空间更少。在这篇演讲中我们会了解这项新技术是如何工作的，以及机器人出租车的未来会是什么样子的。

【中英文稿】
I'm Aicha Evans, I am from Senegal, West Africa, and I fell in love with technology, science and engineering at a very young age.
我是艾莎·埃文斯，我来自西非的一个国家，塞内加尔。在我很小的时候，我就爱上了科技，科学和工程。

Three things happened. I was studying in Paris, and starting at seven years old, flying back and forth between Dakar, Senegal and Paris as an unaccompanied minor. So it wasn't just about the travel. It was really about a portal to knowledge, different environments and adapting.
那时候发生了三件事。那时我在巴黎读书，从7岁开始上学，在达喀尔、塞内加尔和巴黎，三地之间飞来飞去，是旅途无人陪伴的未成年人。所以这对我不仅仅是旅途，更是让我收获了知识，应对不同环境时，学会了适应。

Second thing that happened was every time I was at home in Senegal, I wanted to talk to my friends in Paris. So my dad got tired of the long-distance bills, so he put a little lock on the phone -- the rotary phone. I said, OK, no problem, hacked it, and he kept getting the bills. Sorry again, Dad, if you’re watching this someday.
第二件发生的事情是，每次我在塞内加尔的家里，我都想和我在巴黎的朋友聊天。我爸爸不想支付那些长途话费账单，所以他给电话加了把小锁，那种转盘拨号电话。我说，好啊，没问题。把锁给破解打开了。于是他又收到长途话费账单了。爸爸，如果你某天看到了这个视频，那实在不好意思了。

And then, obviously, the internet was also emerging.
后来，显然互联网开始蓬勃发展了。

So what really happened was that, in terms of technology, I really saw it as something that shaped your experiences, how you understand the world and wanting to be part of it. And for me, the common thread is that physical and virtual transportation -- because that's really what that rotary phone was for me -- are at the center of the innovation flywheel. Now, fast-forward. I'm here today, I'm part of a movement and an industry that is working on bringing transportation and technology together. Huh. It's not just about your commutes. It's really about changing everything in terms of how we move people, goods and services, eventually.
所以真正发生的事，就科技而言，我真的认为它可以改变你的经历，影响你如何认识这个世界，并且想成为它的一部分。对于我来说，那些经历的共同点就是，实体和虚拟的传输。因为当时转盘拨号电话，对我的真正意义就是创新飞轮的中心。我们快进到现在，我今天在这里，也是作为整个科技行业的一部分，致力于将交通和科技结合在一起。哈，这不仅仅只是影响到你的通勤，这是在改变一切，改变人们的出行、货物的运输，最终改变服务业。

That transformation involves robotaxis. Driverless cars again, really? Yeah, yeah, yeah, I've heard it before. And by the way, they are always coming the next decade, and oh, by the way, there's an alphabet soup of companies working on it and we can't even remember who's who and who's doing what. Yeah?
这种转变涉及到无人驾驶出租车。又是无人驾驶汽车，对吧？ 对，对，对，我听过这种说法，顺便说一句，无人驾驶汽车，在未来几十年一定会出现。再顺便说一句，有很多顶尖技术的公司都在研究这个，我们甚至记不清，谁是谁，谁在做什么，是吧？

Audience: Yeah.
观众：是

AE: Yeah, OK, well, this is not about personal, self-driving cars. Sorry to disappoint you. This is really about a few things. First of all, personally and individually owned cars are a wasteful expense, and they contribute to, basically, a lot of pollution and also traffic in urban areas. Second of all, there’s this notion of self-driving shuttles, but frankly, they are optimized for many. They can’t take you specifically from point A to point B. OK, now we have -- hm, how am I going to say this -- the so-called "personal, self-driving" cars of today. Well, the reality is that those cars still require a human behind the wheel. A safety driver. Make no mistake about it. I own one of those, and when I'm in it, I am a safety driver.
好的，我要说的不是私人的自动驾驶汽车。如果让你失望了，那抱歉了。我要说的是其他一些事。首先，个人拥有私家车 是一种属于浪费的开支。基本上，只会造成大量污染，以及加重城区里的交通拥堵程度。其次，我们还有一个概念叫，自动驾驶班车，其实就在于服务更多的人群，不能专门带你从A点到B点。好，我们现在讲了这两个。嗯，我接下来要说的是，那些今天所谓的私人自动驾驶汽车。实际上仍然需要人来驾驶，一个安全驾驶的司机。不要误会，我有一辆这样的车，我在车里，就是那个安全驾驶的司机。

So the question now becomes, What do we do with this? Well, we think that robotaxis, first of all, they will take you specifically from point A to point B. Second of all, when you're not using them, somebody else will be using them. And they are being tested today. When I say that we're on the cusp of finally delivering that vision, there's actually reason to believe it.
那么现在问题变成了，对此我们该怎么办？ 我们来讲讲无人驾驶出租车。首先，它们可以专门带你从A点到B点。其次，当你不用它们的时候，别人会使用它们。现在无人驾驶出租车已经开始测试了，当我说我们即将实现这样的设想，你完全有理由可以相信我。

At the core of self-driving technology is computer vision. Computer vision is a real-time representation, digital representation, of the world and the interactions within it. It has benefited from leaps and bounds of advancements thanks to computer, sensors, machine learning and software innovation. At the core of computer vision are camera systems. Cameras basically help you see agents such as cars, their locations and their actions, pedestrians, their locations, their actions and their gestures.
自动驾驶技术的核心是计算机视觉。计算机视觉就是对环境的实时呈现，对于这个世界和其内部交互的数字呈现。计算机视觉技术收益于现代科技突飞猛进的发展，尤其是计算机、传感器、机器学习，和软件创新的发展。计算机视觉技术的核心是摄像系统。摄像机可以帮你查看周遭环境，像汽车以及它们的位置和行动，行人他们的位置，他们的行为和手势。

In addition, there's also been a lot of advancements. So one example is our vehicle can see the skeleton framework to show you the direction of travel; also to give you details, like, are you dealing with a construction worker in a construction zone or are you dealing with a pedestrian that's probably distracted because they are looking on their phone?
除此之外，还有其他很多进步的地方。比如，车上可以看到代码框架告诉你行驶的方向，还能告诉你其他一些细节信息。 比如，告诉你是否会在施工区域遇到建筑工人，或者你是否碰到了在查看手机而分心的行人。

Now the reality, though -- and this is where it gets interesting -- is that the camera and the algorithms that help us really cannot yet match the human brain's ability to understand and interpret the environment. They just can't. Even though they provide you really high-resolution imaging that really gives you continuous coverage, that doesn't get fatigued, impaired or, you know, drunk or anything like that, at the end of the day, there are still things that they can't see and they can't measure. So if we want autonomous-driving robotaxis soon, we have to supplement cameras.
然而现在的实际情况，也是它变得有趣的地方，就是辅助人类的摄像机和算法，尚无法和人类大脑的能力相提并论，无法像人那样理解和解释环境。就是做不到啊。即使它们可以给你提供高分辨率的成像，真正为你提供连续的实况图像，不会疲劳，受损，醉酒，或其他类似的情况。到头来，仍然有一些它们看不到、无法测量的情况。因此，如果我们想要尽快实现无人驾驶出租车，我们必须要有足够多的摄像机。

Let me walk through some examples. So radar gives you the direction of travel and measures the agent's movement within centimeters per second. Lidar gives you objects and shapes in the real world using depth perception as well as long-range and the all-important night vision. And let me tell you about this, because this is important to me personally and people who look like me. Then you have, also, long-wave infrared where you are able to see agents that are emitting heat, such as animals and humans. And that's again, especially at night, super important.
我们来看一些例子，雷达告诉你行驶的方向，还测量物体的运动，精确到厘米每秒的范围。激光雷达用深度感知。远距离和夜视功能来为你提供现实世界中的物体和其形状的信息。让我再告诉你，因为这对我个人和跟我相似的人来说很重要，就是还有长波红外线。你可以靠这个看到周围散发热量的物体，像动物和人类 一样的道理。这功能在晚上格外重要。

Now, every one of these sensors is very powerful by itself, but when you put them together is when the magic happens. If you see with this vehicle, for example, you have these multiple sensor modalities at all top four corners of the vehicle that basically provide you a 360-degree field of vision, continuously, in a redundant manner, so that we don't miss anything. And this is that same thing with all of the different outputs fused together. And looking at this, basically, and looking at what we see and how we are able to process the data, then learn, then continue to improve our driving, is what tells us that we have confidence, this is the right approach and this time it’s actually coming.
每一个传感器各自的功能都非常强大，结合在一起，就是见证魔法的时刻。如果你看到这样一辆车，在车身四个角上装有这些不同传感器，它们基本上可以为你提供360度的视野，持续不断地以绝不放过任何细节的态度来提供信息。这样我们就不会错过任何信息。其实原理都是一样的，只是把不同输出的信息汇集在一起。这样看，基本上，就是收集我们看到的信息数据， 然后看如何处理这些数据，再学习，然后继续改进自动驾驶的技术。这让我们更有信心，这是正确的方法。以及这一次，它真的要实现了。

Now, this is not, by the way, a brand new concept, OK? Humans have been basically using vision systems to assist them for a long time. Let me back up the boat a little bit, because I know there's a question that everybody's asking, which is, "Hey, how are you going to deal with all the scenarios out there on the streets today?" Most of us are drivers, and it's complicated out there. Well, the truth is that there will always be edge scenarios that sit at the boundary of our real-world testing or that are just too dangerous to test on real streets. That is the truth, and it will be the truth for a very long time. Human beings are pretty underrated in their abilities. So what we do is we use simulation. And with simulation, we're able to construct millions of scenarios in a fabricated environment so that we can see how our software would react.
顺便一提，这不是全新的概念，好吧？ 人类使用视觉系统来辅助他们的生活，已经有很长一段时间了。我把这个问题退后一步来解释。因为我知道大家都很好奇一个问题，就是，如何处理 所有在街上可能会发生的情况？ 我们大部分人都会驾驶，路上的各种交通情况很复杂，事实上，确实会有很极端的情况是我们在真实世界测试很难做到的，或者在真实街道上测试太危险了。这是事实。而且这样的事实会持续很长时间。人类严重低估了自己的能力，所以我们要做的就是运用模拟，通过模拟我们可以在虚构的环境中构建出百万个场景，这样就能评估我们的软件反应如何。

And that's the simulation footage. You can see we're building the world, we're putting in scenarios and we can add things, remove things and see how we would react.
这就是模拟镜头.你可以看到，我们构建虚拟的世界。我们设定了各种场景，我们可以添加一些东西，也可以拿掉一些东西，再看软件会如何反应。

In addition, we have what's called a human in the loop. This is very similar to aviation systems today. We don't want the vehicle to get stuck, and there are rare times where it's not going to know what to do. So we have a team of teleguidance operators that are sitting at a control center, and if the vehicle knows that it's going to be stuck or it doesn't know what to do, it asks for guidance and help and it receives it remotely and then it proceeds.
此外，我们还有所谓的 “有人参与其中“ 这和当今的航空系统非常相似。我们不希望车子陷入无法应对的情况，极少数情况它不知道该怎么，所以我们有一组远程指导操作的人员。他们坐在控制中心，如果车子知道自己要卡住了，或者它不知道怎么做，它可以向指导操作员请求帮助，接着，它远程接受指令，再执行收到的指令。

Now, none of these really are new concepts, as I alluded to earlier. Vision systems have been assisting humans for a long time, especially with things that are not visible to the naked eye. So ... microscopes, right? We've been studying microbes and cells for a long time. Telescopes: we’ve been studying and detecting galaxies millions of light-years away for a long time. And both of these have caused us, for example, to transform industries like medicine, farming, astrophysics and much more.
这些技术都不是什么新的概念，就像我之前说的那样，视觉系统已经辅助人类的生活，很长一段时间了，尤其是帮助勘察人类肉眼看不到的东西。那么像显微镜，对吧？ 我们研究微生物和细胞已经很长一段时间了，望远镜，我们研究和探测数百万光年之外的星系也已经很长一段时间了，而这些都让我们能，举个例子，能改变一些行业，像医药、农业、天体物理学，还有其他更多的行业。

So when we talk about computer vision, when it started, it was really a thought experiment to see if we could replicate what humans see using cameras. It has now graduated with sensors, computers, AI and software innovation to be about surpassing what humans can see and perceive. We've made a lot of progress in this field, but at the end of the day, we have a lot more to do. And with an autonomous robotaxi, you want it to be safe, right and reliable every single time, which requires rigorous testing and optimization. And when that happens and we reach that state, we will wonder how we ever accepted or tolerated 94 percent of crashes being caused by human [error].
所以当我们说计算机系统时，当它刚开始发展，这确实是一场思想的实验，看我们能否用摄像机复制人类看东西的能力。现在它已经配备了传感器，计算机，人工智能，还有软件创新，即将超越人类能看到和认知的能力。我们在这个领域取得了许多进展，但到头来，我们还有很多事情要做。对于无人驾驶出租车，你希望它的每一次出行都是安全、正确和可靠的，这需要严格的测试和优化。到那时候，我们实现那种状态时，我们会想知道我们是如何能接受或容忍94%的事故是由于人为（错误）造成的。

So with computer vision, we have the opportunity to move from problem-solving to problem-preventing. And I truly, truly believe that the next generation of scientists and technologists in, yes, Silicon Valley, but in Paris, in Senegal, West Africa and all over the world, will be exposed to computer vision applied broadly. And with that, all industries will be transformed, and we will experience the world in a different way.
所以有了计算机视觉，我们就有机会从解决问题转向预防问题。我真的相信下一代的科学家和技术人员，不仅仅在硅谷，还要在巴黎，西非的塞纳加尔，以及全世界各地，都能够接触到广泛的计算机视觉应用。有了计算机视觉的广泛应用，所有行业都会改变，我们也将以一种不同的方式体验这个世界。

I hope you can join me in agreeing that this is a gift that we almost owe our next generation that is coming, because there are a lot of things that computer vision will help us solve.
我希望你也和我一样觉得这是我们欠即将到来的下一代的礼物，因为计算机视觉能帮助我们解决许多问题。

Thank you.
谢谢大家。