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.
1885年,卡爾·賓士發明汽車。 那年底, 他開著那台車做了第一次公開試駕, 結果,撞毀了。 過去 130 年來, 我們一直致力於改進 車裡最不可靠的部分,駕駛。 我們讓車子更堅固。 我們加上安全帶和安全氣囊, 過去十年, 我們想辦法讓車子變得更聰明 來修正駕駛這個問題。
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.
現在,我要告訴你 使用駕駛輔助系統來修正這問題, 和完全自動化駕駛其中的差異 以及在世界上所產生的影響。 我也將告訴你 關於我們車子的一些細節 讓你可以了解它所看到的世界 以及如何對外界做出反應, 首先我先來探討一個問題。 是一個大問題: 全世界每年有 120 萬人 死於交通事故。 僅僅美國, 每年就佔了 33,000 人。 依此數據與飛機事故比較, 等於每個上班日, 都有一台 737 從空中掉下。 有點讓人無法置信。 車輛以這種模式賣給我們, 但實際上, 這才是我們開車所遇到的狀況, 對吧?不是晴天,而是雨天, 除了開車外你還想要做其他事。 就是這個原因: 交通變得更糟糕了。 在美國,1990 年到 2010 年間, 汽車行駛哩程數增加百分之三十八, 但是道路只增加百分之六, 所以不是錯覺。 跟幾年前比起來 交通其實是變差。
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.
所有這一切都代表大量的人力成本。 以美國平均通勤時間來看, 大約 50 分鐘, 乘以目前約一億兩千萬工作人口, 將會是六十億分鐘的時間 浪費在每天的通勤上。 這是個很大的數值, 這個數據,相當於 把六十億分鐘的時間 除以一般人的平均壽命, 將會是 162 個人一生的時間 每天浪費在這交通上面, 只是要從 A 點移動到 B 點。 讓人無法置信。 而且,有些在交通上 弱勢的人, 這是史蒂夫。 一個非常有才能力的人, 但是他卻是眼睛失明, 他不是每天早上 花 30 分鐘開車上班, 而是要痛苦的花兩個時 轉搭大眾運輸工具 或請求朋友或親人接送。 他無法像你我一樣 有到處走動的自由。 我們應該做一些事情。
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.
在現在傳統的思慮會提到 我們可以使用一些駕駛輔助系統 我們則持續的推動及改善整個系統, 隨著時間演進, 轉變為自動駕駛系統。 在這我要告訴你們就像我所說的 如果我可以很努力的跳躍, 有一天我可以飛上天 我們確實需要 用一些不同的方式去做事情。 我將告訴你 在自動駕駛系統 與駕駛輔助系統之間, 有三個不同點。 從我們本身的經驗開始談起。
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.
2013 年。 我們第一次做自動駕駛車的測試 我們讓一般大眾人去開它。 嗯,幾乎算是 -- 他們是 100 位谷歌的員工, 但他們的工作都不屬於這個專案, 我們提供車輛 並允許在每天的日常生活中使用。 但這不像是自動駕駛的車, 上頭有一個大大的星號在車上, 他們必須集中注意力開車, 因為這些只是實驗車輛。 我們測試很多, 但仍有失敗狀況發生。 所以我們給車主 2 個小時的訓練, 再讓他們進入車內使用它, 我們所收到的回覆讓人感到驚訝, 當一個新產品嘗試進入這個世界。 每一個人都跟我們說他們愛上了它。 事實上,第一天有一個 開保時捷的駕駛跟我們說, 「這完全是件愚蠢的事情, 不知道我們在想些什麼?」 但是在他測試結束後,他說 「不該只有我可以使用它, 每個人都該使用它, 因為許多人都是個糟糕的駕駛。」 這對我們來說有很大的鼓勵, 然後我們開始研究 人們在車裡做些什麼, 真讓人大開眼界。 我最喜歡的故事裡有一位男士 他低頭看手機 發現電池快沒電了, 然後在車裡像這樣轉過身來, 並且在背包裡找尋東西, 拿出一台筆記型電腦, 放在前座, 再回頭一次, 繼續搜尋,拿出手機的充電線, 轉身回來, 把電源線接上筆電跟手機。 當然他的手機已經開始充電了。 同時他以 65 英哩的速度 在高速公路上行駛。 讓人無法置信。 我們思考這整件事情, 有明顯的結論 有更好的科技輔助, 駕駛就越不可靠。 若只讓車子變得更聰明, 那就和我們希望達到的結果不同。
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.
讓我來談論有關技術的部分。 我們來看這圖型,底部的部分, 是在不該踩煞車的情況下 卻踩煞車的頻率, 你可以忽略大部分的 X 軸, 因為如果在你開車到鎮上的路程中, 隨時煞車的話, 你將不會買這部車。 從垂直軸上 可以看到當車輛踩煞車後 可以幫助你避免意外的頻率。 現在,我們看到左下角這個點, 這是我們一般的車輛。 他不會幫忙煞車 傻傻的也不會幫忙任何事, 當然也無法幫你避免意外。 現在, 如果我們想要引進駕駛輔助系統, 如碰撞減輕煞車系統。 我們將在這上面導入一些技術方案, 由這個曲線得知, 這系統可以發揮一些功效, 但仍不可能避免所有的意外, 因為尚未有足夠的能力。 但我們可以在這個曲線中挑一點, 它也許就足夠避免掉一半 人為疏失所造成的意外, 非常神奇,對吧? 我們改變一、兩個因素 就可以把路上的意外事故減少一半。 在美國每年有接近 一萬七千人 死於交通事故。
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.
但如果我們想要有自動駕駛車輛, 我們需要像這條的技術曲線。 需要將更多的感測器放在車上, 將功能調整在曲線上這一點 基本上這點不會導致車禍發生。 就算是有,也是個很低的機率。 當然你可以跟我辯論這一部分 曲線是否有增量性,我只能說 有些事情就像「80-20 法則」, 向上移動成為一個新的曲線 是非常困難的。 我們暫時由另一個方向來看這件事。 可以發現科技 做正出確的判斷有多高。 這條綠線代表的是駕駛輔助系統。 結果說明了一般駕駛 的錯誤行為而導致意外發生 在美國約每十萬英哩發生一次。 相對的,自動駕駛系統做出決定 每秒大約 10 次, 在這個數量級, 大約每英哩 1,000 次。 所以如果你比較這兩個的距離, 大約是 10 的 8 次方,對吧? 8 次方的數量級。 這個對比有點像是 以我跑步的速度 與光速作比較。 所以不管我如何努力訓練, 都不可能實際達到那個程度。 他們之間有一個很大的差距。
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.
最後的部分, 這個系統可以處理一些突發狀況。 這個人有可能是走在馬路上, 也有可能不是。 我不能預測, 我們的演算法也無法預測, 但是駕駛輔助系統在這情況下, 無法採取任何行動, 如果無預期的踩煞車 是完全無法被接受的。 當自動駕駛系統發現這位行人會說, 我不知道他們打算做什麼, 減慢速度,仔細觀察, 之後再採取更適當的回應。
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.
比起駕駛輔助系統它將會更為安全。 這是兩個不同系統之間的差別。 我們花點時間來探討 車輛所看到的世界。
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?
這是我們的測試車。 從理解目前所在的位置開始, 比對地圖與感應到的訊息, 然後把當下所看到的訊息 再加上另一訊息。 在裏頭,你所看到的所有紫色方框 都是路上的其他車輛, 旁邊的紅色部分則是自行車, 如果你仔細看 上方較遠處, 可以看到一些三角錐。 然後就可以知道車輛當時的位置, 但是我們需要做得更好: 要能夠預測出將會發生的事情, 右上角有一輛小貨卡 將會切換到左邊車道 因為前方的路段將會關閉, 所以需要變更車道 可以預測小貨車的行徑 是件很棒的事, 但我們還需要知道每個人的想法, 這變成一個非常複雜的問題。 有了這資訊後,我們便可以 推測出當下車輛該如何反應。 該跟隨哪一條路線, 該多快反應減速或加速。 匯集所有項目後 只要跟隨著路線, 向左或向右轉動方向盤, 加速或踩油門。 只要這兩個數值 就可以持續到一天結束。 所以會有多難呢?
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.
在 2009 年我們剛開始時, 我的系統看起來像這樣。 你可以看到在中心有我們的車輛, 路上還有其他小方框, 行駛在高速公路上。 這輛測試車需要知道它現在位置 以及其他車輛大約位置。 用幾何方式來理解這個世界。 當開始行駛在近郊及街道中時, 這問題又變為更複雜的層次。 可以看到行人及車輛 都會在我們前面穿過, 往各個方向移動, 紅綠燈,行人穿越道。 相對而言 這是個相當複雜的問題。 一旦這問題可以被解決掉, 車輛就有辦法去處理 這建構出來的環境。 如果左邊有三角錐 它就會要求往右邊開, 當然不只是個封閉的施工環境。 它也還必須去處理 有人走在施工區的路段。 當然,如果有人違規,警察在場 車輛必須知道車頂上有閃著燈的車輛 代表的是警車而不是一般車輛。 相似的情況下, 在路旁的橘色小方框, 是一輛校車, 我們也必需對它做出不同的回應。
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.
當車輛在行駛的時候, 有些人會預期, 當自行車騎士舉起他們的手臂, 是預期汽車可以注意到他們 並且挪出空間 讓他們可以變換車道。 當一位警察站在路上, 測試車輛必須了解要停下來, 如果手勢指揮通行的話, 則要繼續走。
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.
經由交通工具資料共享 我們完成這個成就。 首先,最原始的模型 當車輛遇到施工區域, 讓其他人收到這個訊息 然後它會選擇正確的車道 而避開施工的地方。 但我們對這狀況有更進一步的了解。 取得車子所看到的歷史資料, 數十萬的行人,自行車, 以及視線內的車輛 理解他們看起來像什麼 再用來推斷其他車輛的樣式 及其他行人的長相。 最重要的是, 我們會依此作為模型 以及預測他們是如何移動, 黃色方框指的是 一位行人從我們面前穿越。 藍色方框指的是自行車 而且我們預測 他們將會沿著車輛的右邊前行。 。 這是另一輛自行車從對向而來 而且我們知道他會沿著道路過來。 另外有一個人要右轉, 同時正前方有一個人 正準備要迴轉, 我們可以預測這個行為 並安全的反應。
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 that tells you how to deal with that, but our vehicles were able to encounter that, slow down, and drive safely. Now, we don't have to deal with just ducks. Watch this bird fly across in front of us. The car reacts to that. Here we're dealing with a cyclist that you would never expect to see anywhere other than Mountain View. And of course, we have to deal with drivers, even the very small ones. Watch to the right as someone jumps out of this truck at us. And now, watch the left as the car with the green box decides he needs to make a right turn at the last possible moment. Here, as we make a lane change, the car to our left decides it wants to as well. And here, we watch a car blow through a red light and yield to it. And similarly, here, a cyclist blowing through that light as well. And of course, the vehicle responds safely. And of course, we have people who do I don't know what sometimes on the road, like this guy pulling out between two self-driving cars. You have to ask, "What are you thinking?" (Laughter)
這些是我們一般常見的好的狀況, 當然有時也會遇到一些事情 是之前從不曾遇到過。 幾個月前, 測試車輛行經山景城時, 我們遇到一個狀況。 一位坐著電動輪椅的女人 在路上追逐著繞圈圈鴨子。 (笑聲) 在加州管理局中的駕駛手冊中 找不到任何說明 告訴你如何處理以上狀況, 但是我們測試車輛有辦法處理它。 減速,安全地行駛而過。 我們不只是要對付鴨子。 看到一隻鳥在前方飛越而過 車子也對它們做出反應。 這裡我們正在應付一位自行車騎士 除了在山景城外 你從來無法預期會遇到的。 當然,我們還得應付一些駕駛, 甚至是很小事也要處理。 注意右邊有一個人 在我們面前從卡車上跳下來。 左邊有綠色方框所代表的車輛 在最後一個的關頭決定右轉。 當我們決定要變換車道時, 左邊的車輛 也決定要變換車道。 這邊我們看到一輛車子闖紅燈 就讓他先過。 相同的情況, 則是另一輛自行車闖紅燈。 當然測試車輛可以安全地回應。 有時候人們會在路上 做一些無法理解的事 就像這位仁兄一樣,直接把車輛停在兩輛自動駕駛車之間。 你就會很想問,「你在想些什麼?」 (笑聲)
Now, I just fire-hosed you with a lot of stuff there, so I'm going to break one of these down pretty quickly. So what we're looking at is the scene with the cyclist again, 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, and that comes from the laser data. And that's not actually really easy to understand, so what I'm going to do is I'm going to turn that laser data and look at it, and if you're really good at looking at laser data, you can see a few dots on the curve there, right there, and that blue box is that cyclist. Now as our light is red, the cyclist's light has turned yellow already, and if you squint, you can see that in the imagery. But the cyclist, we see, is going to proceed through the intersection. Our light has now turned green, his is solidly red, and we now anticipate that this bike is going to come all the way across. Unfortunately the other drivers next to us were not paying as much attention. They started to pull forward, and fortunately for everyone, this cyclists reacts, avoids, and makes it through the intersection. And off we go.
我剛才描述了許多狀況給大家, 我將用很快的方式 來分析其中一個狀況。 我們再回到自行車這個例子, 可以注意到下方這部分, 我們還無法真正的看到自行車, 但是車輛可以: 是這個藍色小方框的部分, 這資訊是由雷射所得來的。 確實無法很容易去理解, 我將要做的是轉換雷射資料 然後再來看, 如果你對分析雷射資料很拿手, 你將可以看到 在曲線上面的一些小點, 就在這上面, 上面藍色的小方框就是自行車, 這時我們是紅燈, 自行車這邊已經轉變為黃燈, 如果你斜眼看的話, 可以從這張圖案看到。 這輛我們所看到的自行車, 將打算穿越這個路口。 我們的燈號已經轉為綠燈, 他的則是紅燈, 我們預測這輛自行車 將會穿越整個路口。 不巧的是旁邊的 其他司機並未注意到這點。 他們開始往前移動, 不過很幸運的是, 自行車反應很快地避開, 而且穿越了路口。 結束後我們才往前。
Now, as you can see, we've made some pretty exciting progress, and at this point we're pretty convinced this technology is going to come to market. We do three million miles of testing in our simulators every single day, so you can imagine the experience that our vehicles have. We are looking forward to having this technology on the road, and we think the right path is to go through the self-driving rather than driver assistance approach because the urgency is so large. In the time I have given this talk today, 34 people have died on America's roads.
就如你所見的, 我們有了一些卓越的進展, 在這個階段我們很有自信 這個技術是可以上市的。 在模擬系統下 我們每天做三百萬哩的測試, 你可以想像我們車輛的豐富經歷。 我們正設法把這技術 用於實際道路上, 我們認為正確的方向 應該是自動駕駛 而不是駕駛輔助系統 因為有迫切的需求。 在我今天演說的同時, 有 34 個美國人死於交通事故。
How soon can we bring it out? Well, it's hard to say because it's a really complicated problem, but these are my two boys. My oldest son is 11, and that means in four and a half years, he's going to be able to get his driver's license. My team and I are committed to making sure that doesn't happen.
我們可以多快讓它上市? 嗯,這很難說 因為這是一個很複雜的問題, 這是我兩個兒子。 大的兒子 11 歲,表示再 4 年半, 他就可以拿到駕照。 我跟我的團隊承諾 確保不會讓這件事情發生。
Thank you.
謝謝。
(Laughter) (Applause) Chris Anderson: Chris, I've got a question for you.
(笑聲)(掌聲) 克里斯·安德森: 克里斯,我有一個問題。
Chris Urmson: Sure.
克里斯·厄姆森:好。
CA: So certainly, the mind of your cars is pretty mind-boggling. On this debate between driver-assisted and fully driverless -- I mean, there's a real debate going on out there right now. So some of the companies, for example, Tesla, are going the driver-assisted route. What you're saying is that that's kind of going to be a dead end because you can't just keep improving that route and get to fully driverless at some point, and then a driver is going to say, "This feels safe," and climb into the back, and something ugly will happen.
克里斯·安德森: 的確,你車輛的智慧系統讓人驚訝。 尤其在輔助駕駛與 自動駕駛的辯論中, 現在有一個真實的辯論就存在那邊。 有一些公司,如:特斯拉, 正在研究一些駕駛輔助系統。 根據你所說的 這個發展將會是個死胡同 因為無法藉由改善輔助系統 最後完全取代自動駕駛 在某一點上,駕駛可能會說, 「這感覺到是安全的」, 然後爬到後座去, 一些可怕的事情就可能會發生。
CU: Right. No, that's exactly right, and it's not to say that the driver assistance systems aren't going to be incredibly valuable. They can save a lot of lives in the interim, but to see the transformative opportunity to help someone like Steve get around, to really get to the end case in safety, to have the opportunity to change our cities and move parking out and get rid of these urban craters we call parking lots, it's the only way to go.
克里斯·厄姆森: 完全正確,現在還無法說 駕駛輔助系統不具有價值。 在這段過渡期間 它們仍可挽救許多生命, 但是看到這是一個改變的機會, 可以幫助像史蒂夫一樣的人, 而且最終是個安全的方案, 去擁有這機會去改變我們的城市 可以擺脫城市裡一個個的停車場, 這是唯一的辦法。
CA: We will be tracking your progress with huge interest. Thanks so much, Chris. CU: Thank you. (Applause)
克里斯·安德森: 我們非常有興趣持續追蹤你的進度 謝謝你,克里斯。 克里斯·厄姆森:謝謝。(掌聲)