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: Če bi avti lahko govorili, bi se lahko izognili nesrečam

Filmed:
908,454 views

Ko vozimo, smo v steklenem balonu, z zaklenjenimi vrati, pritikamo na plin in zanašamo se na naše oči, četudi lahko vidimo le nekaj avtov pred nami in nekaj za nami. Ampak kaj če bi lahko avtomobili med sabo posredovali informacije o položaju in hitrosti in bi z modeli predvidevanja izračunali najvarnejšo pot za vse na cesti? Jennifer Healey si predstavlja svet brez prometnih nesreč. (Posnetek: 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 faceobraz it:
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Priznajmo si:
00:14
DrivingVožnja is dangerousnevarno.
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Vožnja je nevarna.
00:17
It's one of the things that we don't like to think about,
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Je ena od stvari o katerih ne razmišljamo radi,
00:20
but the factdejstvo that religiousreligiozno iconsikone and good luckSreča charmsčare
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ampak dejstvo, da se religiozne ikone in nalepke za srečo
00:23
showshow up on dashboardsnadzorne plošče around the worldsvet
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pojavljajo na armaturnih ploščah po svetu,
00:28
betraysizdal the factdejstvo that we know this to be trueresnično.
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priča o dejstvu, da se tega zavedamo.
00:32
CarAvto accidentsnesreče are the leadingvodil causevzrok of deathsmrt
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Prometne nesreče so glavni povzročitelj smrti
00:36
in people agesstarosti 16 to 19 in the UnitedVelika StatesDržave --
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pri ljudeh od 16 do 19 let, v Združenih Državah --
00:40
leadingvodil causevzrok of deathsmrt --
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glavni vzrok smrti --
00:43
and 75 percentodstotkov of these accidentsnesreče have nothing to do
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in 75 procentov teh nesreč ni v povezavi
00:47
with drugsdroge or alcoholalkohol.
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z drogami ali alkoholom.
00:49
So what happensse zgodi?
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Torej, kaj se dogaja?
00:51
No one can say for sure, but I rememberZapomni si my first accidentnesreča.
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Nihče ne more zagotovo vedeti,
ampak spomnim se svoje prve nesreče.
00:55
I was a youngmladi drivervoznik out on the highwayavtocesta,
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Bila sem mladi voznik na avtocesti
00:59
and the caravto in frontspredaj of me, I saw the brakezavore lightsluči go on.
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in pri avtu pred mano sem videla, da so se prižgale zavorne luči.
01:02
I'm like, "Okay, all right, this guy is slowingupočasnjuje down,
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Pomislila sem "V redu, ta upočasnjuje,
01:03
I'll slowpočasi down too."
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tudi samo bom upočasnila."
01:05
I stepkorak on the brakezavore.
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Pritisnila sem na zavoro.
01:07
But no, this guy isn't slowingupočasnjuje down.
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Ampak ne, ta pred mano ne upočasnjuje.
01:09
This guy is stoppingustavi, deadmrtev stop, deadmrtev stop on the highwayavtocesta.
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Ta se ustavlja, popolnoma, ustavlja se sredi avtoceste.
01:12
It was just going 65 -- to zeronič?
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Šel je iz 65 (100 km/h) -- do nič?
01:15
I slammedSlammed on the brakeszavore.
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Pritisnila sem na zavoro.
01:16
I feltčutil the ABSABS kickkick in, and the caravto is still going,
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Čutila sem, da se je uklopil ABS in avto je še kar peljal,
01:19
and it's not going to stop, and I know it's not going to stop,
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in ne bo se ustavil, in vem, da se ne bo ustavil,
01:22
and the airzrak bagvreča deploysrazvije, the caravto is totaledskupaj,
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in zračna blazina se je sprožila, avto je bil totalka.
01:25
and fortunatelyna srečo, no one was hurtboli.
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Na srečo nihče ni bil poškodovan.
01:28
But I had no ideaideja that caravto was stoppingustavi,
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Ampak nisem vedela, da se bo tisti avto ustavil,
01:32
and I think we can do a lot better than that.
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in mislim, da smo sposobni česa boljšega.
01:36
I think we can transformpreoblikovati the drivingvožnja experienceizkušnje
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Mislim, da lahko preoblikujemo vožnjo,
01:40
by lettingdajanje v najem our carsavtomobili talk to eachvsak other.
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s tem, da dovolimo, da se naši avti pogovarjajo med sabo.
01:44
I just want you to think a little bitbit
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Želim, da malo pomislite
01:46
about what the experienceizkušnje of drivingvožnja is like now.
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kako je dandanes voziti.
01:48
Get into your caravto. CloseBlizu the doorvrata. You're in a glasssteklo bubblemehurček.
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Sedeš v avto. Zapreš vrata. Si v steklenem mehurčku.
01:53
You can't really directlyneposredno sensesmisel the worldsvet around you.
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Ne moreš imeti neposrednega občutka sveta okoli tebe.
01:55
You're in this extendedpodaljšan bodytelo.
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Si v tem povečanem telesu.
01:58
You're taskednalogo with navigatingnavigacijo it down
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Tvoja naloga je, da ga usmerjaš
02:00
partially-seendelno videl roadwaysceste,
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po delno vidnih cestah,
02:02
in and amongstmed other metalkovinski giantsvelikani, at super-humansuper človekovih speedshitrosti.
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med drugimi jeklenimi velikani, pri super-človeški hitrosti.
02:06
Okay? And all you have to guidevodnik you are your two eyesoči.
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Ja? In vse kar imaš kot vodilo, sta tvoji dve očesi.
02:11
Okay, so that's all you have,
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Torej, to je vse kar imaš,
02:12
eyesoči that weren'tni bilo really designedzasnovan for this tasknaloga,
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oči, ki niso bile ustvarjene za to nalogo.
02:14
but then people askvprašajte you to do things like,
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Potem ljudje rečejo, stvari, kot so:
02:18
you want to make a lanepasu changesprememba,
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Hočeš zamenjati pas,
02:20
what's the first thing they askvprašajte you do?
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kaj je prva stvar, ki ti jo naročijo?
02:22
Take your eyesoči off the roadcesta. That's right.
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Nehaj gledati na cesto. Tako je prav.
02:25
Stop looking where you're going, turnobrat,
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Ne glej kam greš, obrni se,
02:27
checkpreveri your blindslepi spotspot,
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preveri mrtve kote,
02:29
and drivepogon down the roadcesta withoutbrez looking where you're going.
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in vozi po cesti, ne da bi gledal kam pelješ.
02:33
You and everyonevsi elsedrugače. This is the safevarno way to drivepogon.
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Ti in vsi ostali. To je varen način vožnje.
02:36
Why do we do this? Because we have to,
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Zakaj to počnemo? Ker moramo.
02:38
we have to make a choiceizbira, do I look here or do I look here?
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Moram sprejeti odločitev, naj gledam sem ali naj gledam sem.
02:40
What's more importantpomembno?
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Kaj je bolj pomembno?
02:42
And usuallyobičajno we do a fantasticfantastično jobdelo
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In po navadi nam gre odlično
02:45
pickingnabiranje and choosingizbira what we attendse udeležijo to on the roadcesta.
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z izbiranjem in razmišljanjem čemu na cesti se posvetimo.
02:48
But occasionallyobčasno we misszgrešiti something.
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Ampak včasih kaj spregledamo.
02:52
OccasionallyObčasno we sensesmisel something wrongnarobe or too latepozen.
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Včasih kaj zaznamo narobe ali pa prepozno.
02:57
In countlessnešteto accidentsnesreče, the drivervoznik sayspravi,
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V nešteto nesrečah, voznik pravi:
02:59
"I didn't see it comingprihajajo."
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"Nisem ga videl."
03:01
And I believe that. I believe that.
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In temu verjamem. Temu verjamem.
03:04
We can only watch so much.
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Vsega ne moremo videti.
03:07
But the technologytehnologijo existsobstaja now that can help us improveizboljšati that.
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Ampak danes imamo tehnologijo, ki nam lahko pomaga, da to izboljšamo.
03:12
In the futureprihodnost, with carsavtomobili exchangingizmenjavo datapodatkov with eachvsak other,
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V prihodnosti, ko si bodo avti izmenjevali podatke,
03:17
we will be ablesposoben to see not just threetri carsavtomobili aheadnaprej
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ne bomo videli le za tri avte naprej,
03:20
and threetri carsavtomobili behindzadaj, to the right and left,
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ampak za tri avte nazaj, v desno in levo,
03:22
all at the sameenako time, bird'sptičje eyeoči viewpogled,
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vse to hkrati, iz ptičje perspektive,
03:25
we will actuallydejansko be ablesposoben to see into those carsavtomobili.
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pravzaprav bomo videli v tiste avte.
03:28
We will be ablesposoben to see the velocityhitrost of the caravto in frontspredaj of us,
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Videli bomo lahko hitrost avta, ki je pred nami,
03:31
to see how fasthitro that guy'sfant je going or stoppingustavi.
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da vidimo kako hitro pospešuje ali se ustavlja.
03:34
If that guy'sfant je going down to zeronič, I'll know.
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Če se popolnoma ustavlja, bom vedela.
03:38
And with computationračunanje and algorithmsalgoritmi and predictivepredvidevanje modelsmodeli,
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In z izračuni in algoritmi in modeli predvidevanja,
03:42
we will be ablesposoben to see the futureprihodnost.
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bomo lahko videli v prihodnost.
03:46
You maylahko think that's impossiblenemogoče.
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Lahko se vam zdi nemogoče.
03:47
How can you predictnapovedati the futureprihodnost? That's really hardtežko.
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Kako lahko predvidiš prihodnost? To je težko.
03:50
ActuallyDejansko, no. With carsavtomobili, it's not impossiblenemogoče.
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V bistvu ne. Z avti ni nemogoče.
03:54
CarsAvtomobili are three-dimensionaltridimenzionalno objectspredmetov
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Avti so tridimenzionalni predmeti
03:56
that have a fixeddoločen positionpoložaj and velocityhitrost.
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s stalnim položajem in hitrostjo.
03:59
They travelpotovanje down roadsceste.
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Potujejo po cesti.
04:00
OftenPogosto they travelpotovanje on pre-publishedvnaprej objavljena routespoti.
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Pogosto potujejo po vnaprej zastavljeni poti.
04:03
It's really not that hardtežko to make reasonablerazumno predictionsnapovedi
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Res ni tako težko ustvariti razumnih predvidevanj
04:07
about where a car'savto going to be in the nearblizu futureprihodnost.
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glede tega, kje bo avto v prihodnosti.
04:09
Even if, when you're in your caravto
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Tudi, če si v avtu
04:11
and some motorcyclistmotorist comesprihaja -- bshoombshoom! --
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in pride mimo motorist --bshoom!--
04:13
85 milesmilje an houruro down, lane-splittingpas deljenja --
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85 milj na uro (140 km/h).
04:16
I know you've had this experienceizkušnje --
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Vem, da ste to že doživeli --
04:18
that guy didn't "just come out of nowherenikjer."
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ta motorist ni "kar prišel od nikoder".
04:21
That guy'sfant je been on the roadcesta probablyverjetno for the last halfpol houruro.
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Ta motorist je bil na cesti verjetno zadnje pol ure.
04:25
(LaughterSmeh)
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(Smeh)
04:26
Right? I mean, somebody'snekdo je seenvidel him.
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Res? Mislim, gotovo ga je nekdo videl.
04:29
TenDeset, 20, 30 milesmilje back, someone'snekdo je seenvidel that guy,
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10, 20, 30 milj nazaj, nekdo ga je videl
04:32
and as soonkmalu as one caravto seesvidi that guy
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in takoj, ko ga en avto vidi
04:34
and putsstavi him on the mapzemljevid, he's on the mapzemljevid --
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in ga postavi na zemljevid, je na zemljevidu --
04:37
positionpoložaj, velocityhitrost,
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položaj, hitrost,
04:39
good estimateoceniti he'llon bo continuenadaljuj going 85 milesmilje an houruro.
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lahko rečemo, da bo pot nadaljeval pri 85 mph (140 km/h).
04:41
You'llBoste know, because your caravto will know, because
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Ti boš vedel, ker bo vedel tvoj avto, ker
04:43
that other caravto will have whisperedšepetal something in his earuho,
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mu bo tisti drugi avto zašepetal na uho,
04:46
like, "By the way, fivepet minutesminut,
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npr.: "Mimogrede, pet minut,
04:48
motorcyclistmotorist, watch out."
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motorist, pazi."
04:50
You can make reasonablerazumno predictionsnapovedi about how carsavtomobili behaveObnašajte se.
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Lahko narediš razumna predvidevanja o vedenju avtov.
04:53
I mean, they're NewtonianNewtonovo objectspredmetov.
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Mislim, saj so 'Newtonski' predmeti.
04:54
That's very nicelepo about them.
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To je pri njih zelo prikladno.
04:57
So how do we get there?
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Torej, kako do tega?
05:00
We can startZačni with something as simplepreprosto
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Začnemo lahko z nečim preprostim,
05:03
as sharingdelitev our positionpoložaj datapodatkov betweenmed carsavtomobili,
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kot je izmenjava podatkov o polažaju med avti,
05:05
just sharingdelitev GPSGPS.
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samo z delitvijo GPS-ja.
05:07
If I have a GPSGPS and a camerakamera in my caravto,
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Če imam GPS in kamero v avtu,
05:10
I have a prettylepa precisenatančno ideaideja of where I am
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se mi kar zdi kje sem
05:12
and how fasthitro I'm going.
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in kako hitro peljem.
05:14
With computerračunalnik visionvizijo, I can estimateoceniti where
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Z računalniškim pogledom lahko predvidim kje
05:15
the carsavtomobili around me are, sortRazvrsti of, and where they're going.
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so avti okoli mene, nekako, in kam grejo.
05:19
And sameenako with the other carsavtomobili.
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In enako je z ostalimi avti.
05:20
They can have a precisenatančno ideaideja of where they are,
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Lahko natančno vedo kje so,
05:22
and sortRazvrsti of a vaguenejasen ideaideja of where the other carsavtomobili are.
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in se jim zdi kje so ostali avti.
05:24
What happensse zgodi if two carsavtomobili sharedeliti that datapodatkov,
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Kaj se zgodi, če si dva avta delita podatke,
05:27
if they talk to eachvsak other?
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če se pogovarjata?
05:29
I can tell you exactlytočno what happensse zgodi.
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Lahko vam povem natanko kaj se zgodi.
05:32
BothOba modelsmodeli improveizboljšati.
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Oba modela se izboljšata.
05:34
EverybodyVsi winszmaga.
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Vsi imajo korist.
05:36
ProfessorProfesor BobBob WangWang and his teamekipa
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Profeso Bob Wang in njegova ekipa
05:39
have doneKončano computerračunalnik simulationssimulacije of what happensse zgodi
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so naredili računalniško simulacijo kaj se zgodi,
05:42
when fuzzymehka estimatesocene combinezdružiti, even in lightsvetloba trafficprometa,
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ko se približna predvidenja združi, celo pri malo prometa,
05:45
when carsavtomobili just sharedeliti GPSGPS datapodatkov,
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kjer si avti le delijo GPS podatke.
05:48
and we'vesmo movedpreselil this researchraziskave out of the computerračunalnik simulationsimulacija
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In to raziskavo smo premaknili iz računalniške simulacije
05:50
and into robotrobot testtest bedspostelje that have the actualdejansko sensorssenzorji
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v robotske testne neprave s senzorji,
05:53
that are in carsavtomobili now on these robotsroboti:
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ki so v avtih, zdaj na te robote:
05:56
stereostereo cameraskamere, GPSGPS,
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stereo kamere, GPS
05:58
and the two-dimensionaldvodimenzionalno laserlaser rangerazpon findersiskala
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in dvodimenzionalne laserske pregledovalce območja,
06:00
that are commonpogosti in backuprezerva systemssistemov.
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ki so navadno v vzvratnih sistemih.
06:02
We alsotudi attachpriložite a discretediskreten short-rangekratkega dosega communicationkomunikacija radioradio,
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Dodali smo tudi diskreten radio za komunikacijo na kratke razdalje,
06:07
and the robotsroboti talk to eachvsak other.
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in roboti se med sabo pogovarjajo.
06:09
When these robotsroboti come at eachvsak other,
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Ko se ti roboti približajo eden drugemu,
06:10
they tracksledi eachvsak other'sdrugi positionpoložaj preciselynatančno,
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si izmenjajo natančne podatke o položaju
06:13
and they can avoidizogibajte se eachvsak other.
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in lahko se eden drugemu izogibajo.
06:16
We're now addingdodajanje more and more robotsroboti into the mixmešamo,
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Zdaj dodajamo več in več robotov
06:19
and we encounterednaletel some problemstežave.
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in odkrili smo nekaj problemov.
06:21
One of the problemstežave, when you get too much chatterCvrkutanje,
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Eden od problemov se pojavi, ko je preveč tega pogovarjanja,
06:23
it's hardtežko to processproces all the packetspaketov, so you have to prioritizeprednost,
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potem je težko obdelati toliko podatkov, zato je treba nekatere prioritizirati
06:27
and that's where the predictivepredvidevanje modelmodel helpspomaga you.
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in s tem nam pomagajo modeli predvidevanja.
06:29
If your robotrobot carsavtomobili are all trackingsledenje the predictednapovedano trajectoriestrajektorije,
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Če vaši robotski avti sledijo predvidenim potem,
06:33
you don't payplačati as much attentionpozornost to those packetspaketov.
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se ni potrebno posvečati vsem njim.
06:35
You prioritizeprednost the one guy
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Prednost daš tistemu,
06:37
who seemsZdi se to be going a little off courseseveda.
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za katerega se vidi,
da mogoče ne bo sledil načrtu.
06:38
That guy could be a problemproblem.
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Ta zna biti problem.
06:41
And you can predictnapovedati the newnovo trajectorytrajektorija.
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In lahko predvidiš novo pot.
06:44
So you don't only know that he's going off courseseveda, you know how.
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Tako da ne veš samo da pelje izven načrta, ampak veš kako.
06:46
And you know whichki driversvozniki you need to alertopozorilo to get out of the way.
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In veš katere voznike je potrebno opozoriti, da se umaknejo s poti.
06:50
And we wanted to do -- how can we bestnajboljši alertopozorilo everyonevsi?
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In hoteli smo narediti -- kako lahko najbolje opozorimo vse?
06:53
How can these carsavtomobili whisperšepet, "You need to get out of the way?"
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Kako lahko ti avti šepetajo "Moraš se umakniti s poti."?
06:56
Well, it dependsodvisno on two things:
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No, odvisno je od dveh stvari:
06:58
one, the abilitysposobnost of the caravto,
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prva je zmožnost avta
07:00
and seconddrugič the abilitysposobnost of the drivervoznik.
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in druga je sposobnost voznika.
07:03
If one guy has a really great caravto,
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Če ima nekdo res dober avto,
07:04
but they're on theirnjihovi phonetelefon or, you know, doing something,
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ampak telefonira ali počne kaj drugega,
07:07
they're not probablyverjetno in the bestnajboljši positionpoložaj
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potem ni v najboljšem položaju,
07:09
to reactreagirati in an emergencynujno.
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da bi reagiral v sili.
07:12
So we startedzačel a separateločeni lineline of researchraziskave
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Zato smo začeli ločeno raziskavo
07:14
doing drivervoznik statedržava modelingmodeliranje.
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o modeliranju voznikovega stanja.
07:16
And now, usinguporabo a seriesserije of threetri cameraskamere,
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In zdaj, z uporabo treh kamer
07:19
we can detectodkriti if a drivervoznik is looking forwardnaprej,
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zaznamo, če voznik gleda naprej,
07:21
looking away, looking down, on the phonetelefon,
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stran, dol, na telefon
07:24
or havingimeti a cupskodelico of coffeekava.
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ali sreba kavo.
07:27
We can predictnapovedati the accidentnesreča
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Lahko predvidimo nesreče
07:29
and we can predictnapovedati who, whichki carsavtomobili,
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in lahko predvidimo kdo in kateri avto,
07:33
are in the bestnajboljši positionpoložaj to movePremakni se out of the way
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sta na najboljšem položaju, da se umakneta
07:36
to calculateizračunati the safestnajvarnejši routepot for everyonevsi.
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za najvarnejšo pot vseh.
07:39
FundamentallyBistveno, these technologiestehnologije existobstajajo todaydanes.
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Temeljno, te tehnologije danes obstajajo.
07:44
I think the biggestnajvečji problemproblem that we faceobraz
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Mislim, da je največja težava s katero se soočamo
07:47
is our ownlastno willingnesspripravljenost to sharedeliti our datapodatkov.
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naša lastna volja za izmenjavo podatkov.
07:50
I think it's a very disconcertingnenavaden notionpojma,
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Misli, da gre za zaskrbljujoče mišljenje,
07:52
this ideaideja that our carsavtomobili will be watchinggledal us,
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ta misel, da nas bodo avti opazovali,
07:55
talkinggovoriti about us to other carsavtomobili,
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da bodo govorili o nas z drugimi avti,
07:58
that we'llbomo be going down the roadcesta in a seamorje of gossipopravljanje.
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da se bomo vozili po cesti v morju govoric.
08:02
But I believe it can be doneKončano in a way that protectsščiti our privacyzasebnost,
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Ampak verjamem, da je lahko storjeno tako, da je naša privatnost zaščitena,
08:05
just like right now, when I look at your caravto from the outsidezunaj,
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tako kot zdaj, ko pogledam vaš avto od zunaj,
08:09
I don't really know about you.
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v resnici ne vem nič o vas.
08:12
If I look at your licenselicenco plateplošča numberštevilka,
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Če pogledam vašo registracijo,
08:13
I don't really know who you are.
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ne vem kdo ste.
08:15
I believe our carsavtomobili can talk about us behindzadaj our backshrbet.
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Verjamem, da lahko naši avti govorijo o nas za našimi hrbti.
08:19
(LaughterSmeh)
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(Smeh)
08:22
And I think it's going to be a great thing.
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In mislim, da bo to dobro.
08:25
I want you to considerrazmislite for a momenttrenutek
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Hočem, da razmislite za trenutek,
08:27
if you really don't want the distractedmoti teenagernajstnik behindzadaj you
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če res nočete, da bi raztresena najstnica za vami
08:31
to know that you're brakingzaviranje,
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vedela, da zavirate,
08:33
that you're comingprihajajo to a deadmrtev stop.
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da se popolnoma ustavljate.
08:36
By sharingdelitev our datapodatkov willinglyhote,
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S tem, da delimo podatke,
08:38
we can do what's bestnajboljši for everyonevsi.
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storimo kar je najboljše za vse.
08:41
So let your caravto gossipopravljanje about you.
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Torej, dovolite, da vas vaš avto opravlja.
08:44
It's going to make the roadsceste a lot safervarnejše.
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S tem bodo ceste veliko varnejše.
08:47
Thank you.
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Hvala.
08:49
(ApplauseAplavz)
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(Aplavz)
Translated by Jure Mavrič
Reviewed by Kaja Kren

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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