Let's face it: Driving is dangerous. It's one of the things that we don't like to think about, but the fact that religious icons and good luck charms show up on dashboards around the world betrays the fact that we know this to be true. Car accidents are the leading cause of death in people ages 16 to 19 in the United States -- leading cause of death -- and 75 percent of these accidents have nothing to do with drugs or alcohol.
面對現實吧 開車是危險的 這是我們不願去想的事 但現實中我們在世界各地看到 汽車儀器板上的宗教畫像和平安符 卻揭露我們其實知道開車危險 車禍是美國16至19歲青年 死亡的主因 車禍是導致死亡的主因 而當中百分之七十五的車禍 和毒品或酒精無關
So what happens? No one can say for sure, but I remember my first accident. I was a young driver out on the highway, and the car in front of me, I saw the brake lights go on. I'm like, "Okay, all right, this guy is slowing down, I'll slow down too." I step on the brake. But no, this guy isn't slowing down. This guy is stopping, dead stop, dead stop on the highway. It was just going 65 -- to zero? I slammed on the brakes. I felt the ABS kick in, and the car is still going, and it's not going to stop, and I know it's not going to stop, and the air bag deploys, the car is totaled, and fortunately, no one was hurt. But I had no idea that car was stopping, and I think we can do a lot better than that. I think we can transform the driving experience by letting our cars talk to each other.
到底發生了什麼? 沒人能確實說出 我記得我第一次車禍 當時我還年輕 在高速公路上 看到前面車輛的煞車燈亮了 我心想﹕好吧 這傢伙慢了下來 那我也跟著慢下來吧 我跟著踩煞車 但我錯了 他不是減慢速度 而是停車 竟然在高速公路上停車 從時速六十五英哩降到... 零? 我急踩剎車 ABS系統啟動了 但車仍向前駛 車繼續向前 我知道車不會停了 氣囊彈出 車子毀了 幸好沒有傷亡 我完全沒想到那輛車會停 我認為我們可以做得更好 我們可以透過讓車之間對話 改變駕駛的方式
I just want you to think a little bit about what the experience of driving is like now. Get into your car. Close the door. You're in a glass bubble. You can't really directly sense the world around you. You're in this extended body. You're tasked with navigating it down partially-seen roadways, in and amongst other metal giants, at super-human speeds. Okay? And all you have to guide you are your two eyes. Okay, so that's all you have, eyes that weren't really designed for this task, but then people ask you to do things like, you want to make a lane change, what's the first thing they ask you do? Take your eyes off the road. That's right. Stop looking where you're going, turn, check your blind spot, and drive down the road without looking where you're going. You and everyone else. This is the safe way to drive. Why do we do this? Because we have to, we have to make a choice, do I look here or do I look here? What's more important? And usually we do a fantastic job picking and choosing what we attend to on the road. But occasionally we miss something. Occasionally we sense something wrong or too late. In countless accidents, the driver says, "I didn't see it coming." And I believe that. I believe that. We can only watch so much.
大家想一想 現在我們是怎樣開車的 上車 關上門 在一個玻璃安全室裡 你無法直接感受周遭的世界 在這個擴大了的軀殼裡 你的任務是 在看不清路面的情況下 在其他高速行駛的車輛間行駛 只有你的眼睛能幫助你 對 就只有你的眼睛 我們的眼睛不是用來完成這任務的 可是人們總要求你做這樣的事 例如你要換條車道 第一件要做的事是什麼? 不看路面的情況 是的 不去看你前進的方向 然後轉彎 檢查是不是有盲點 不看路 一直向前開 在座各位和其他人 都會認為這是安全駕駛 為什麼要這樣做? 因為我們必須 必須選擇 是要看這邊 還是那邊 更重要的是甚麼? 通常我們都能在路上 好好挑選我們要走的路 但偶爾我們也會出小差池 有時候我們意識到做錯決定 已經太遲 無數的車禍 司機都說 沒看到有車駛過來 我相信這種說法 非常相信 我們能看到的有限
But the technology exists now that can help us improve that. In the future, with cars exchanging data with each other, we will be able to see not just three cars ahead and three cars behind, to the right and left, all at the same time, bird's eye view, we will actually be able to see into those cars. We will be able to see the velocity of the car in front of us, to see how fast that guy's going or stopping. If that guy's going down to zero, I'll know.
不過現今科技可以幫助我們 將來只要車輛間能互相交換資訊 我們不僅可以看到前面三部輛車 還能看到後面三輛車 右邊的 左邊的 全都看到 就像在空中俯瞰 我們能夠實際觀察其他車 我們可以知道前面車輛的速度 其他司機開得多快或是否要停車 他要停車 我也會知道
And with computation and algorithms and predictive models, we will be able to see the future. You may think that's impossible. How can you predict the future? That's really hard. Actually, no. With cars, it's not impossible. Cars are three-dimensional objects that have a fixed position and velocity. They travel down roads. Often they travel on pre-published routes. It's really not that hard to make reasonable predictions about where a car's going to be in the near future. Even if, when you're in your car and some motorcyclist comes -- bshoom! -- 85 miles an hour down, lane-splitting -- I know you've had this experience -- that guy didn't "just come out of nowhere." That guy's been on the road probably for the last half hour. (Laughter) Right? I mean, somebody's seen him. Ten, 20, 30 miles back, someone's seen that guy, and as soon as one car sees that guy and puts him on the map, he's on the map -- position, velocity, good estimate he'll continue going 85 miles an hour. You'll know, because your car will know, because that other car will have whispered something in his ear, like, "By the way, five minutes, motorcyclist, watch out." You can make reasonable predictions about how cars behave. I mean, they're Newtonian objects. That's very nice about them.
透過電腦計算、規則系統和預知模擬 我們可以預知未來 或許大家會覺得不可能 我們怎能預知未來 這太難了 其實這不難 車輛可以做到 車是立體的 有固定的位置和速度 在路上行駛 通常在規劃好的路上行駛 要合理地預測 車子在短時間內 會怎樣 並不困難 即使你駕駛時 遇到幾台機車 咻 ! 以時速85英哩呼嘯而過 車子紛紛讓道 相信大家都有這經驗吧 那人可不是憑空出現 他說不定都已經在路上跑了半小時 [笑聲] 是吧?我是說 一定有人看到他 10哩前 20哩前 30哩前, 一定有人看到他 而只要有一部車看到他 把他放到地圖上 大家都會知道 他的位置和速度 估計他會以每小時85英哩速度持續前進 你的車子預報 你便會知道這訊息 其他車也會對司機碎碎念 就像: 順便一提 再過5分鐘 便會有機車 要小心 你可以合理地預測車在路的情況 我是說 車子也遵從牛頓定律 還好是這樣
So how do we get there? We can start with something as simple as sharing our position data between cars, just sharing GPS. If I have a GPS and a camera in my car, I have a pretty precise idea of where I am and how fast I'm going. With computer vision, I can estimate where the cars around me are, sort of, and where they're going. And same with the other cars. They can have a precise idea of where they are, and sort of a vague idea of where the other cars are. What happens if two cars share that data, if they talk to each other? I can tell you exactly what happens. Both models improve. Everybody wins. Professor Bob Wang and his team have done computer simulations of what happens when fuzzy estimates combine, even in light traffic, when cars just share GPS data, and we've moved this research out of the computer simulation and into robot test beds that have the actual sensors that are in cars now on these robots: stereo cameras, GPS, and the two-dimensional laser range finders that are common in backup systems. We also attach a discrete short-range communication radio, and the robots talk to each other. When these robots come at each other, they track each other's position precisely, and they can avoid each other.
那 我們要如何走到那一步? 我們可以開始分享我們車與車的位置 從這件小事做起 就只是分享 GPS (全球定位系統) 如果我的車子裝有 GPS 和鏡頭 我就能精確地知道我所在的位置 以及我是以多快的速度前進 有電腦輔助 我還可以估算我旁邊哪裡有車 以及他們要往哪裡去 其他車也可以知道 他們可以知道自己身在何方 也能大約了解其他車子的位置 那麼如果兩部車子分享資訊, 會如何呢? 假設它們可以對話呢? 我可以告訴你會發生什麼事 兩部車都獲得行進間的改善 是雙贏 Bob Wang 教授和他的團隊 做了一個電腦模擬 看下列情況會產生什麼結果 當集合一些粗估的資料 用在行車順暢的情況下, 讓車與車只是交換 GPS 資訊 我們將這個研究帶離電腦模擬 帶到裝有感應器的機器實驗上 把原本放車子的裝置, 用到機器身上 音響, 鏡頭, GPS 還有平面雷射測距儀 這些都是車子的標準配備 我們還裝上一台短距的無線電機台 讓機器可以相互溝通 當兩部機器朝彼此前進 它們可以追蹤到彼此的精確位置 因此可避免互撞
We're now adding more and more robots into the mix, and we encountered some problems. One of the problems, when you get too much chatter, it's hard to process all the packets, so you have to prioritize, and that's where the predictive model helps you. If your robot cars are all tracking the predicted trajectories, you don't pay as much attention to those packets. You prioritize the one guy who seems to be going a little off course. That guy could be a problem. And you can predict the new trajectory. So you don't only know that he's going off course, you know how. And you know which drivers you need to alert to get out of the way.
我們現在在這個實驗中加入越來越多的機器 然後我們遇到了一些狀況 其一便是, 太吵了 當接收到太多瑣碎的言語 就很難去處理所有的資訊封包 這時就得選擇優先順序 而預知模型系統 在這個時候就派上用場了 如果機器人車子 一直行駛在可預期的軌道上 就不用費心去解讀那些資訊封包 首先被挑出的這個人 往往就是有點偏移行進路線 那麼這個人就有可能是個麻煩 然後你就可以再規劃一條新的軌道 所以你不只知道他要偏離航道了 還知道他要怎麼偏 也能知道遇到那個駕駛要提高警覺, 離遠一點
And we wanted to do -- how can we best alert everyone? How can these cars whisper, "You need to get out of the way?" Well, it depends on two things: one, the ability of the car, and second the ability of the driver. If one guy has a really great car, but they're on their phone or, you know, doing something, they're not probably in the best position to react in an emergency. So we started a separate line of research doing driver state modeling. And now, using a series of three cameras, we can detect if a driver is looking forward, looking away, looking down, on the phone, or having a cup of coffee. We can predict the accident and we can predict who, which cars, are in the best position to move out of the way to calculate the safest route for everyone. Fundamentally, these technologies exist today.
我們希望 -- 我們要如何警告其他人? 這些車要怎麼小聲警告: "你得趕緊離開這裡" 嗯, 這得要兩個條件配合 一, 車子的性能 二, 駕駛本身的技巧 就算這個人擁有一部很讚的車 但是他如果邊開邊做其他事 像講手機之類的 他們可能就無法在緊急狀態中 做出最佳的反應 因此我們又著手另一項實驗 做有關駕駛者的狀態模型 我們使用三個一組的攝像機 來偵測這個駕駛人是看前面 看別的地方, 看下面, 還是講手機 或喝咖啡 我們可以預知事故 我們可以知道是誰以及哪一部車子 最好離開目前的道路 並且幫每一個人規劃最安全的路徑 最重要的是 這些科技在今天都已經有了
I think the biggest problem that we face is our own willingness to share our data. I think it's a very disconcerting notion, this idea that our cars will be watching us, talking about us to other cars, that we'll be going down the road in a sea of gossip. But I believe it can be done in a way that protects our privacy, just like right now, when I look at your car from the outside, I don't really know about you. If I look at your license plate number, I don't really know who you are. I believe our cars can talk about us behind our backs.
我認為眼前最大的困難是 大家是否願意做資訊分享 我想這樣的概念或許讓人有點不安 因為我們的車子會監視著我們 還跟別的車子打小報告 簡直就像航行在一片流言蜚語的汪洋中 但是我相信, 我們還是可以 在保有隱私的情況下執行 好比現在, 如果我從外面看你的車 我不會知道你是何許人物 就算看著你的牌照號碼 我也不會知道你是誰 我相信我們的車子會在我們背後說長道短
(Laughter)
[笑聲]
And I think it's going to be a great thing. I want you to consider for a moment if you really don't want the distracted teenager behind you to know that you're braking, that you're coming to a dead stop. By sharing our data willingly, we can do what's best for everyone.
但我認為那將會是件好事 花幾分鐘想想 你是不是真的不希望 那煩人的青少年在你背後 知道你要煞車 知道你要停下來 倘若各位願意分享 我們可以幫每個人做到最好
So let your car gossip about you. It's going to make the roads a lot safer.
所以 讓你的車去東家長西家短吧 這能讓道路使用更為安全
Thank you.
謝謝各位
(Applause)
[掌聲]