ABOUT THE SPEAKER
Jennifer Healey - Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future.

Why you should listen

Jennifer Healey imagines a future where computers and smartphones are capable of being sensitive to human emotions and where cars are able to talk to each other, and thus keep their drivers away from accidents. A scientist at Intel Corporation Research Labs, she researches devices and systems that would allow for these major innovations.

Healey holds PhD from MIT in electrical engineering and computer science. While there, she pioneered “Affective Computing” with Rosalind Picard and developed the first wearable computer with physiological sensors and a video camera that allows the wearer to track their daily activities and how they feel while doing them. From there, she moved to IBM where she worked on the next generation of multi-modal interactive smartphones and helped architect the "Interaction Mark-Up language" that allows users to switch from voice to speech input seamlessly.

Healey has also used her interest in embedded devices in the field of healthcare. While an instructor at Harvard Medical School and at Beth Israel Deaconess Medical Center, she worked on new ways to use heart rate to predict cardiac health. She then joined HP Research in Cambridge to further develop wearable sensors for health monitoring and continued this research when she joined Intel Digital Health.

More profile about the speaker
Jennifer Healey | Speaker | TED.com
TED@Intel

Jennifer Healey: If cars could talk, accidents might be avoidable

Jennifer Healey: 如果車子會說話, 就能避免事故發生

Filmed:
908,454 views

開車就像是坐在一顆玻璃泡泡內, 關上門, 踩油門, 只能依靠你的雙眼來引導方向 -- 即使自己只能看到前後幾部車輛。那麼如果車子可以彼此分享各自的位置和速度等資訊, 然後運用預估模型幫每一部車去計算安全路徑呢? Jennifer Healey 帶我們想像一個沒有事故的世界。 (攝於TED@Intel)
- Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future. Full bio

Double-click the English transcript below to play the video.

00:12
Let's face面對 it:
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面對現實吧
00:14
Driving駕駛 is dangerous危險.
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開車是危險的
00:17
It's one of the things that we don't like to think about,
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這是我們不願去想的事
00:20
but the fact事實 that religious宗教 icons圖標 and good luck運氣 charms魅力
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但現實中我們在世界各地看到
00:23
show顯示 up on dashboards儀表板 around the world世界
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汽車儀器板上的宗教畫像和平安符
00:28
betrays原形畢露 the fact事實 that we know this to be true真正.
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卻揭露我們其實知道開車危險
00:32
Car汽車 accidents事故 are the leading領導 cause原因 of death死亡
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車禍是美國16至19歲青年
00:36
in people ages年齡 16 to 19 in the United聯合的 States狀態 --
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死亡的主因
00:40
leading領導 cause原因 of death死亡 --
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車禍是導致死亡的主因
00:43
and 75 percent百分 of these accidents事故 have nothing to do
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而當中百分之七十五的車禍
00:47
with drugs毒品 or alcohol.
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和毒品或酒精無關
00:49
So what happens發生?
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到底發生了什麼?
00:51
No one can say for sure, but I remember記得 my first accident事故.
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沒人能確實說出
我記得我第一次車禍
00:55
I was a young年輕 driver司機 out on the highway高速公路,
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當時我還年輕 在高速公路上
00:59
and the car汽車 in front面前 of me, I saw the brake制動 lights燈火 go on.
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看到前面車輛的煞車燈亮了
01:02
I'm like, "Okay, all right, this guy is slowing減緩 down,
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我心想﹕好吧 這傢伙慢了下來
01:03
I'll slow down too."
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那我也跟著慢下來吧
01:05
I step on the brake制動.
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我跟著踩煞車
01:07
But no, this guy isn't slowing減緩 down.
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但我錯了 他不是減慢速度
01:09
This guy is stopping停止, dead stop, dead stop on the highway高速公路.
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而是停車 竟然在高速公路上停車
01:12
It was just going 65 -- to zero?
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從時速六十五英哩降到... 零?
01:15
I slammed抨擊 on the brakes剎車.
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我急踩剎車
01:16
I felt the ABSABS kick in, and the car汽車 is still going,
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ABS系統啟動了 但車仍向前駛
01:19
and it's not going to stop, and I know it's not going to stop,
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車繼續向前 我知道車不會停了
01:22
and the air空氣 bag deploys展開時, the car汽車 is totaled總計,
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氣囊彈出 車子毀了
01:25
and fortunately幸好, no one was hurt傷害.
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幸好沒有傷亡
01:28
But I had no idea理念 that car汽車 was stopping停止,
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我完全沒想到那輛車會停
01:32
and I think we can do a lot better than that.
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我認為我們可以做得更好
01:36
I think we can transform轉變 the driving主動 experience經驗
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我們可以透過讓車之間對話
01:40
by letting出租 our cars汽車 talk to each other.
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改變駕駛的方式
01:44
I just want you to think a little bit
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大家想一想
01:46
about what the experience經驗 of driving主動 is like now.
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現在我們是怎樣開車的
01:48
Get into your car汽車. Close the door. You're in a glass玻璃 bubble泡沫.
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上車 關上門 在一個玻璃安全室裡
01:53
You can't really directly sense the world世界 around you.
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你無法直接感受周遭的世界
01:55
You're in this extended擴展 body身體.
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在這個擴大了的軀殼裡
01:58
You're tasked任務 with navigating導航 it down
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你的任務是
02:00
partially-seen部分見過 roadways道路,
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在看不清路面的情況下
02:02
in and amongst其中包括 other metal金屬 giants豪門, at super-human超人類 speeds速度.
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在其他高速行駛的車輛間行駛
02:06
Okay? And all you have to guide指南 you are your two eyes眼睛.
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只有你的眼睛能幫助你
02:11
Okay, so that's all you have,
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對 就只有你的眼睛
02:12
eyes眼睛 that weren't really designed設計 for this task任務,
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我們的眼睛不是用來完成這任務的
02:14
but then people ask you to do things like,
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可是人們總要求你做這樣的事
02:18
you want to make a lane車道 change更改,
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例如你要換條車道
02:20
what's the first thing they ask you do?
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第一件要做的事是什麼?
02:22
Take your eyes眼睛 off the road. That's right.
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不看路面的情況 是的
02:25
Stop looking where you're going, turn,
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不去看你前進的方向 然後轉彎
02:27
check your blind spot,
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檢查是不是有盲點
02:29
and drive駕駛 down the road without looking where you're going.
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不看路 一直向前開
02:33
You and everyone大家 else其他. This is the safe安全 way to drive駕駛.
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在座各位和其他人
都會認為這是安全駕駛
02:36
Why do we do this? Because we have to,
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為什麼要這樣做? 因為我們必須
02:38
we have to make a choice選擇, do I look here or do I look here?
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必須選擇 是要看這邊 還是那邊
02:40
What's more important重要?
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更重要的是甚麼?
02:42
And usually平時 we do a fantastic奇妙 job工作
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通常我們都能在路上
02:45
picking選擇 and choosing選擇 what we attend出席 to on the road.
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好好挑選我們要走的路
02:48
But occasionally偶爾 we miss小姐 something.
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但偶爾我們也會出小差池
02:52
Occasionally偶爾 we sense something wrong錯誤 or too late晚了.
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有時候我們意識到做錯決定 已經太遲
02:57
In countless無數 accidents事故, the driver司機 says,
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無數的車禍 司機都說
02:59
"I didn't see it coming未來."
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沒看到有車駛過來
03:01
And I believe that. I believe that.
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我相信這種說法 非常相信
03:04
We can only watch so much.
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我們能看到的有限
03:07
But the technology技術 exists存在 now that can help us improve提高 that.
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不過現今科技可以幫助我們
03:12
In the future未來, with cars汽車 exchanging交換 data數據 with each other,
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將來只要車輛間能互相交換資訊
03:17
we will be able能夠 to see not just three cars汽車 ahead
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我們不僅可以看到前面三部輛車
03:20
and three cars汽車 behind背後, to the right and left,
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還能看到後面三輛車 右邊的 左邊的
03:22
all at the same相同 time, bird's鳥類 eye view視圖,
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全都看到 就像在空中俯瞰
03:25
we will actually其實 be able能夠 to see into those cars汽車.
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我們能夠實際觀察其他車
03:28
We will be able能夠 to see the velocity速度 of the car汽車 in front面前 of us,
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我們可以知道前面車輛的速度
03:31
to see how fast快速 that guy's傢伙 going or stopping停止.
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其他司機開得多快或是否要停車
03:34
If that guy's傢伙 going down to zero, I'll know.
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他要停車 我也會知道
03:38
And with computation計算 and algorithms算法 and predictive預測 models楷模,
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透過電腦計算、規則系統和預知模擬
03:42
we will be able能夠 to see the future未來.
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我們可以預知未來
03:46
You may可能 think that's impossible不可能.
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或許大家會覺得不可能
03:47
How can you predict預測 the future未來? That's really hard.
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我們怎能預知未來 這太難了
03:50
Actually其實, no. With cars汽車, it's not impossible不可能.
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其實這不難 車輛可以做到
03:54
Cars汽車 are three-dimensional三維 objects對象
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車是立體的
03:56
that have a fixed固定 position位置 and velocity速度.
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有固定的位置和速度
03:59
They travel旅行 down roads道路.
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在路上行駛
04:00
Often經常 they travel旅行 on pre-published預發布 routes路線.
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通常在規劃好的路上行駛
04:03
It's really not that hard to make reasonable合理 predictions預測
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要合理地預測 車子在短時間內
04:07
about where a car's汽車 going to be in the near future未來.
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會怎樣 並不困難
04:09
Even if, when you're in your car汽車
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即使你駕駛時
04:11
and some motorcyclist摩托車手 comes -- bshoombshoom! --
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遇到幾台機車 咻 !
04:13
85 miles英里 an hour小時 down, lane-splitting車道分割 --
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以時速85英哩呼嘯而過 車子紛紛讓道
04:16
I know you've had this experience經驗 --
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相信大家都有這經驗吧
04:18
that guy didn't "just come out of nowhere無處."
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那人可不是憑空出現
04:21
That guy's傢伙 been on the road probably大概 for the last half hour小時.
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他說不定都已經在路上跑了半小時
04:25
(Laughter笑聲)
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[笑聲]
04:26
Right? I mean, somebody's某人的 seen看到 him.
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是吧?我是說 一定有人看到他
04:29
Ten, 20, 30 miles英里 back, someone's誰家 seen看到 that guy,
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10哩前 20哩前 30哩前, 一定有人看到他
04:32
and as soon不久 as one car汽車 sees看到 that guy
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而只要有一部車看到他
04:34
and puts看跌期權 him on the map地圖, he's on the map地圖 --
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把他放到地圖上 大家都會知道
04:37
position位置, velocity速度,
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他的位置和速度
04:39
good estimate估計 he'll地獄 continue繼續 going 85 miles英里 an hour小時.
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估計他會以每小時85英哩速度持續前進
04:41
You'll你會 know, because your car汽車 will know, because
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你的車子預報 你便會知道這訊息
04:43
that other car汽車 will have whispered低聲道 something in his ear,
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其他車也會對司機碎碎念
04:46
like, "By the way, five minutes分鐘,
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就像: 順便一提 再過5分鐘
04:48
motorcyclist摩托車手, watch out."
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便會有機車 要小心
04:50
You can make reasonable合理 predictions預測 about how cars汽車 behave表現.
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你可以合理地預測車在路的情況
04:53
I mean, they're Newtonian牛頓 objects對象.
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我是說 車子也遵從牛頓定律
04:54
That's very nice不錯 about them.
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還好是這樣
04:57
So how do we get there?
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那 我們要如何走到那一步?
05:00
We can start開始 with something as simple簡單
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我們可以開始分享我們車與車的位置
05:03
as sharing分享 our position位置 data數據 between之間 cars汽車,
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從這件小事做起
05:05
just sharing分享 GPS全球定位系統.
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就只是分享 GPS (全球定位系統)
05:07
If I have a GPS全球定位系統 and a camera相機 in my car汽車,
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如果我的車子裝有 GPS 和鏡頭
05:10
I have a pretty漂亮 precise精確 idea理念 of where I am
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我就能精確地知道我所在的位置
05:12
and how fast快速 I'm going.
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以及我是以多快的速度前進
05:14
With computer電腦 vision視力, I can estimate估計 where
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有電腦輔助
我還可以估算我旁邊哪裡有車
05:15
the cars汽車 around me are, sort分類 of, and where they're going.
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以及他們要往哪裡去
05:19
And same相同 with the other cars汽車.
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其他車也可以知道
05:20
They can have a precise精確 idea理念 of where they are,
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他們可以知道自己身在何方
05:22
and sort分類 of a vague模糊 idea理念 of where the other cars汽車 are.
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也能大約了解其他車子的位置
05:24
What happens發生 if two cars汽車 share分享 that data數據,
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那麼如果兩部車子分享資訊, 會如何呢?
05:27
if they talk to each other?
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假設它們可以對話呢?
05:29
I can tell you exactly究竟 what happens發生.
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我可以告訴你會發生什麼事
05:32
Both models楷模 improve提高.
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兩部車都獲得行進間的改善
05:34
Everybody每個人 wins.
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是雙贏
05:36
Professor教授 Bob短發 Wang and his team球隊
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Bob Wang 教授和他的團隊
05:39
have doneDONE computer電腦 simulations模擬 of what happens發生
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做了一個電腦模擬
看下列情況會產生什麼結果
05:42
when fuzzy模糊 estimates估計 combine結合, even in light traffic交通,
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當集合一些粗估的資料
用在行車順暢的情況下,
05:45
when cars汽車 just share分享 GPS全球定位系統 data數據,
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讓車與車只是交換 GPS 資訊
05:48
and we've我們已經 moved移動 this research研究 out of the computer電腦 simulation模擬
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我們將這個研究帶離電腦模擬
05:50
and into robot機器人 test測試 beds that have the actual實際 sensors傳感器
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帶到裝有感應器的機器實驗上
05:53
that are in cars汽車 now on these robots機器人:
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把原本放車子的裝置, 用到機器身上
05:56
stereo立體聲 cameras相機, GPS全球定位系統,
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音響, 鏡頭, GPS
05:58
and the two-dimensional二維 laser激光 range範圍 finders發現者
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還有平面雷射測距儀
06:00
that are common共同 in backup備用 systems系統.
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這些都是車子的標準配備
06:02
We also attach連接 a discrete離散的 short-range短距離 communication通訊 radio無線電,
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我們還裝上一台短距的無線電機台
06:07
and the robots機器人 talk to each other.
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讓機器可以相互溝通
06:09
When these robots機器人 come at each other,
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當兩部機器朝彼此前進
06:10
they track跟踪 each other's其他 position位置 precisely恰恰,
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它們可以追蹤到彼此的精確位置
06:13
and they can avoid避免 each other.
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因此可避免互撞
06:16
We're now adding加入 more and more robots機器人 into the mix混合,
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我們現在在這個實驗中加入越來越多的機器
06:19
and we encountered遇到 some problems問題.
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然後我們遇到了一些狀況
06:21
One of the problems問題, when you get too much chatter喋喋不休,
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其一便是, 太吵了
當接收到太多瑣碎的言語
06:23
it's hard to process處理 all the packets, so you have to prioritize優先,
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就很難去處理所有的資訊封包
這時就得選擇優先順序
06:27
and that's where the predictive預測 model模型 helps幫助 you.
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而預知模型系統
在這個時候就派上用場了
06:29
If your robot機器人 cars汽車 are all tracking追踪 the predicted預料到的 trajectories軌跡,
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如果機器人車子
一直行駛在可預期的軌道上
06:33
you don't pay工資 as much attention注意 to those packets.
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就不用費心去解讀那些資訊封包
06:35
You prioritize優先 the one guy
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首先被挑出的這個人
06:37
who seems似乎 to be going a little off course課程.
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往往就是有點偏移行進路線
06:38
That guy could be a problem問題.
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那麼這個人就有可能是個麻煩
06:41
And you can predict預測 the new trajectory彈道.
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然後你就可以再規劃一條新的軌道
06:44
So you don't only know that he's going off course課程, you know how.
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所以你不只知道他要偏離航道了
還知道他要怎麼偏
06:46
And you know which哪一個 drivers司機 you need to alert警報 to get out of the way.
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也能知道遇到那個駕駛要提高警覺, 離遠一點
06:50
And we wanted to do -- how can we best最好 alert警報 everyone大家?
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我們希望 -- 我們要如何警告其他人?
06:53
How can these cars汽車 whisper耳語, "You need to get out of the way?"
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這些車要怎麼小聲警告:
"你得趕緊離開這裡"
06:56
Well, it depends依靠 on two things:
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嗯, 這得要兩個條件配合
06:58
one, the ability能力 of the car汽車,
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一, 車子的性能
07:00
and second第二 the ability能力 of the driver司機.
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二, 駕駛本身的技巧
07:03
If one guy has a really great car汽車,
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就算這個人擁有一部很讚的車
07:04
but they're on their phone電話 or, you know, doing something,
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但是他如果邊開邊做其他事
像講手機之類的
07:07
they're not probably大概 in the best最好 position位置
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他們可能就無法在緊急狀態中
07:09
to react應對 in an emergency.
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做出最佳的反應
07:12
So we started開始 a separate分離 line of research研究
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因此我們又著手另一項實驗
07:14
doing driver司機 state modeling造型.
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做有關駕駛者的狀態模型
07:16
And now, using運用 a series系列 of three cameras相機,
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我們使用三個一組的攝像機
07:19
we can detect檢測 if a driver司機 is looking forward前鋒,
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來偵測這個駕駛人是看前面
07:21
looking away, looking down, on the phone電話,
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看別的地方, 看下面, 還是講手機
07:24
or having a cup杯子 of coffee咖啡.
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或喝咖啡
07:27
We can predict預測 the accident事故
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我們可以預知事故
07:29
and we can predict預測 who, which哪一個 cars汽車,
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我們可以知道是誰以及哪一部車子
07:33
are in the best最好 position位置 to move移動 out of the way
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最好離開目前的道路
07:36
to calculate計算 the safest最安全 route路線 for everyone大家.
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並且幫每一個人規劃最安全的路徑
07:39
Fundamentally從根本上, these technologies技術 exist存在 today今天.
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最重要的是
這些科技在今天都已經有了
07:44
I think the biggest最大 problem問題 that we face面對
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我認為眼前最大的困難是
07:47
is our own擁有 willingness願意 to share分享 our data數據.
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大家是否願意做資訊分享
07:50
I think it's a very disconcerting令人不安 notion概念,
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我想這樣的概念或許讓人有點不安
07:52
this idea理念 that our cars汽車 will be watching觀看 us,
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因為我們的車子會監視著我們
07:55
talking about us to other cars汽車,
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還跟別的車子打小報告
07:58
that we'll be going down the road in a sea of gossip八卦.
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簡直就像航行在一片流言蜚語的汪洋中
08:02
But I believe it can be doneDONE in a way that protects保護 our privacy隱私,
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但是我相信, 我們還是可以
在保有隱私的情況下執行
08:05
just like right now, when I look at your car汽車 from the outside,
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好比現在, 如果我從外面看你的車
08:09
I don't really know about you.
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我不會知道你是何許人物
08:12
If I look at your license執照 plate盤子 number,
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就算看著你的牌照號碼
08:13
I don't really know who you are.
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我也不會知道你是誰
08:15
I believe our cars汽車 can talk about us behind背後 our backs.
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我相信我們的車子會在我們背後說長道短
08:19
(Laughter笑聲)
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[笑聲]
08:22
And I think it's going to be a great thing.
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但我認為那將會是件好事
08:25
I want you to consider考慮 for a moment時刻
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花幾分鐘想想
08:27
if you really don't want the distracted分心 teenager青少年 behind背後 you
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你是不是真的不希望
那煩人的青少年在你背後
08:31
to know that you're braking制動,
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知道你要煞車
08:33
that you're coming未來 to a dead stop.
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知道你要停下來
08:36
By sharing分享 our data數據 willingly甘心,
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倘若各位願意分享
08:38
we can do what's best最好 for everyone大家.
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我們可以幫每個人做到最好
08:41
So let your car汽車 gossip八卦 about you.
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所以 讓你的車去東家長西家短吧
08:44
It's going to make the roads道路 a lot safer更安全.
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這能讓道路使用更為安全
08:47
Thank you.
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謝謝各位
08:49
(Applause掌聲)
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[掌聲]
Translated by KA WAI WONG
Reviewed by Emma Chiang

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ABOUT THE SPEAKER
Jennifer Healey - Research scientist
A research scientist at Intel, Jennifer Healey develops the mobile internet devices of the future.

Why you should listen

Jennifer Healey imagines a future where computers and smartphones are capable of being sensitive to human emotions and where cars are able to talk to each other, and thus keep their drivers away from accidents. A scientist at Intel Corporation Research Labs, she researches devices and systems that would allow for these major innovations.

Healey holds PhD from MIT in electrical engineering and computer science. While there, she pioneered “Affective Computing” with Rosalind Picard and developed the first wearable computer with physiological sensors and a video camera that allows the wearer to track their daily activities and how they feel while doing them. From there, she moved to IBM where she worked on the next generation of multi-modal interactive smartphones and helped architect the "Interaction Mark-Up language" that allows users to switch from voice to speech input seamlessly.

Healey has also used her interest in embedded devices in the field of healthcare. While an instructor at Harvard Medical School and at Beth Israel Deaconess Medical Center, she worked on new ways to use heart rate to predict cardiac health. She then joined HP Research in Cambridge to further develop wearable sensors for health monitoring and continued this research when she joined Intel Digital Health.

More profile about the speaker
Jennifer Healey | Speaker | TED.com

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