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.
Godine 1885. Karl Benc izumeo je automobil. Kasnije, u toku godine, izveo ga je na prvu javnu test vožnju i, ovo se zaista dogodilo, udario ga u zid. U poslednjih 130 godina radili smo na tom, najmanje pouzdanom, delu automobila - vozaču. Ojačali smo sam automobil. Dodali smo pojaseve za sedišta, vazdušne jastuke, i, u poslednjoj deceniji, počeli smo čak i da činimo auto pametnijim kako bismo ispravili tu grešku - vozača.
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.
Danas ću govoriti malo o razlici između rešavanja problema sistema asistencije vozaču i zapravo, potpuno samoupravljajućih automobila i šta oni mogu da učine za svet. Takođe ću govoriti ponešto i o našem automobilu i prikazati vam kako on vidi svet, kako reaguje i šta radi, ali prvo moram da kažem nešto i o jednom problemu. A problem je velik: svake godine, na putevima širom sveta, 1,2 miliona ljudi pogine. Samo u Americi svake godine pogine 33 000 ljudi. Stavimo to u ovakvu perspektivu: to je kao da se svakog radnog dana 737 sruši iz vazduha. To je neverovatno. Automobili nam se prodaju na ovakav način ali, zapravo, ovo predstavlja vožnju, zar ne? Nema sunca nego kiše i radije biste radili bilo šta drugo, osim vožnje. A razlog tome je sledeći: saobraćaj postaje sve gori. U Americi, u periodu između 1990. i 2010, milje koje su vozila prelazila se uvećao za 38%. Procenat naših puteva se uvećao za 6%. To nije samo u našim glavama,
And all of this has a very human cost.
saobraćaj se zaista znatno pogoršao u skorije vreme.
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.
I sve to ima veliku cenu po čoveka. Ako uzmete prosečno vreme transporta, u Americi, koje iznosi oko 50 minuta i pomnožite ga sa 120 miliona radnika koje imamo, dobijete negde oko šest milijardi minuta protraćenih tokom transporta svakog dana. To je velika cifra pa sagledajmo to na sledeći način: uzmite tih šest milijardi minuta i podelite ih sa prosečnim životnim vekom jedne osobe i dobićete 162 života uzaludno utrošenih svakog dana, prelazeći samo od tačke A do B. To je neverovatno. Zatim, imamo i one koji nemaju tu privilegiju da sede u saobraćaju. Ovo je Stiv. On je neverovatno sposoban muškarac ali je, pri tom, i slep. A to znači da umesto tridesetominutne jutarnje vožnje do posla, to je iskušenje od dva sata spajajući delove javnog prevoza ili moljakanje prijatelja i porodice za prevoz. On nema tu istu slobodu kretanja koju vi i ja imamo. Trebalo bi da uradimo nešto po tom pitanju.
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.
Konvencionalno razmišljanje bi bilo da uzmemo te sisteme asistencije vozača, da ih guramo, postepeno unapređujemo i da će se oni vremenom pretvoriti u samoupravljajuće automobile. Međutim, to je kao kada bih vam ja sada rekao da ako bih se mnogo trudio da skačem, da bih jednog dana mogao i da poletim. Morali bismo ipak da uradimo nešto drugačije. Zato ću vam pričati o tri glavne razlike između samoupravljajućih automobila i sistema asistencije vozaču. I počeću sa nekim od naših iskustava.
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.
Godine 2013. imali smo prvi test samoupravljajućih automobila i dozvolili običnim ljudima da ga koriste. Dobro, delimično običnim - to je bilo 100 Guglovih radnika međutim, oni nisu radili na projektu. Dali smo im taj automobil i dozvolili im da ga koriste u svakodnevnom životu. U odnosu na druge samoupravljajuće automobile ovaj je imao jednu veliku začkoljicu: morali su da vode računa jer je ovo bilo eksperimentalno vozilo. Mi smo ga dosta testirali, ali je i dalje moglo da se pokvari. Dva sata smo ih obučavali, stavili smo ih u auto, dali im da ga koriste, i šta smo naknadno čuli je bilo fenomenalno za nekoga ko pokušava da uvede proizvod u svet. Svi ponaosob su rekli da im se svideo. Zapravo, imali smo jednog vozača Poršea koji nam je prvog dana rekao: "Ovo je potpuno glupo. O čemu uopšte razmišljamo?" Ali je, na kraju, rekao: "Ne samo da je meni potreban, potreban je svima, jer su ljudi očajni vozači." To je bila muzika za naše uši. A onda smo počeli da gledamo šta ljudi unutar automobila rade i to nam je otvorilo oči. A meni je omiljena priča jednog gospodina koji gleda u svoj telefon i shvati da mu se baterija istrošila. On se ovako okrenuo, u autu, čeprkao po svom rancu, izvukao svoj laptop, stavio ga na sedište, opet se okrenuo nazad, opet čeprkao i izvukao kabl za punjenje za svoj telefon, vrpoljio se, uključio kabl u laptop i u telefon. Zasigurno, telefon se puni a svo to vreme on se vozio brzinom od 100 km na sat, po autoputu. Zaista neverovatno. Razmišljali smo o ovome i rekli smo da je to nekako očigledno. Što više tehnologija napreduje vozač postaje sve manje pouzdan. Samim tim što automobil postepeno postaje pametan verovatno više nećemo videti pobede koje smo nekada želeli.
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.
Dozvolite mi da vam se obratim tehničkim jezikom, na trenutak. Gledajući ovaj grafik, na dnu primećujemo koliko često automobil primenjuje kočnice kada ne bi trebalo. Ignorišite veći deo ove ose jer ako se vozite gradom i automobil iz nekog razloga stane, vi nikada nećete kupiti taj auto. Vertikalna osa prikazuje koliko često će auto primeniti kočnice, kada je to potrebno, da bi vam pomogao da izbegnete nesreću. Ako sada pogledamo u ovaj donji levi ugao - to je vaš klasičan automobil. On ne primenjuje kočnice umesto vas, ne radi ništa šašavo ali isto tako vas i ne čuva od nesreće. Ako sada želimo u auto da uvedemo sistem asistencije vozaču recimo za kočenje za ublažavanje sudara ubacićemo neki paket tehnologije tu i to je ova kriva ovde i sistem će imati neka upravljačka svojstva ali nikada neće izbeći sve nesreće jer nema takve sposobnosti. Ali izabraćemo neku poziciju na krivi ovde pa možda izbegava polovinu nesreća koja ljudima vozačima promakne neverovatno, zar ne? Upravo smo duplo smanjili nesreće na našim putevima. Sada 17 000 ljudi manje pogine u Americi svake godine.
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.
Ali ako želimo samoupravljajući automobil, potrebna nam je kriva koja izgleda ovako. Morali bismo da postavimo više senzora u vozilo i izabraćemo jednu tačku korišćenja ovde gde, zapravo, nikada ne dolazi do sudara. Dešavaće se, ali sa vrlo malom učestalošću. Mogli bismo ovo da gledamo i da polemišemo o tome da li je ovo postepeno i ja bih rekao da je to "pravilo 80-20" i da je veoma teško da se podigne na tu novu krivu. Ali hajde na trenutak da to sagledamo iz drugačijeg ugla. Hajde da vidimo koliko često tehnologija postupi na ispravan način. Ova zelena tačka ovde predstavlja sistem asistencije vozaču. Ispostavilo se da ljudi vozači prave greške koje dovode do saobraćajnih nesreća jednom u 161 000 km, u Americi. Nasuprot tome, sistem asistencije vozaču verovatno donosi odluke oko 10 puta u sekundi, a to je red veličine oko 1000 puta u toku 1,5 kilometra. Ako uporedite razdaljinu između ove dve veličine, to je oko 10 na osmu, zar ne? Osam puta više. To je kao kada bi poredili koliko brzo ja trčim u odnosu na brzinu svetlosti. Nije bitno koliko naporno treniram kada zapravo, nikada neću moći to da stignem. Prema tome, to je vrlo velik procep.
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.
I na kraju, naravno, imamo i to kako sistem podnosi nesigurnosti. Ovaj pešak ovde možda iskorači na put, a možda i ne. Ja ne mogu to da znam, niti bilo koji naš algoritam ali u slučaju sistema asistencije vozaču to znači da ne može nešto da preduzme opet iz razloga što ako nenadano pritisne kočnice to je potpuno neprihvatljivo. Dok samoupravljajući sistem može da prepozna pešaka i kaže "Ne znam šta će da uradi, uspori, pogledaj bolje i nakon toga reaguj adekvatno."
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.
Tako da je mnogo bezbedniji nego što će sistem asistencije vozaču ikad biti. Toliko o razlikama između ova dva sistema. Hajde da sada razgovaramo o tome kako automobil vidi svet.
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?
Dakle, ovo je naše vozilo. Kreće tako što prepoznaje gde se nalazi, na svetu, tako što koristi podatke sa mape i senzora i upoređuje ih i na to dodaje šta vidi u konkretnom trenutku. Tako da ove ljubičaste kutije koje vidite predstavljaju druga vozila na putu. Ove crvene stvari, sa strane, su biciklisti, a tamo u daljini, ako bolje pogledate, možete videti čunjeve. Sad, pošto znamo gde se auto nalazi u datom trenutku moramo da uradimo i bolje od toga: moramo da predvidimo šta će se desiti. Ovaj pikap kamion, gore desno, će da izvrši prestrojavanje u levu traku zato što je napred put zatvoren pa on mora da se skloni sa puta. Poznajemo taj jedan pikap i to je super ali moramo da znamo i o čemu svi ostali misle pa to sad postaje vrlo komplikovan problem. Ako sad to znamo, možemo da smislimo kako bi auto trebalo da reaguje u trenutku koju putanju bi trebalo da sledi, koliko brzo bi trebalo da uspori ili ubrza. I sve to postaje samo praćenje putanje: skretanje levo ili desno, pritiskanje kočnice ili gasa. Na kraju se sve svodi na dva broja, zapravo. Koliko teško to može da bude?
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.
Kada smo počinjali u 2009, ovako je izgledao naš sistem. Možete videti naš automobil u sredini i ostale kocke na putu kako se voze po autoputu. Auto mora da prepozna gde se nalazi i gde se otprilike nalaze i ostala vozila. To je zapravo geometrijsko razumevanje sveta. Kada smo počeli da vozimo po komšiluku i gradskim ulicama, pojavile su se poteškoće na potpuno novom nivou. Vidimo pešake koji prelaze ispred nas, automobile koji prelaze ispred nas koji idu u svim smerovima, semafori, pešački prelazi. To je neverovatno složen problem u odnosu na prethodni. Jednom kada je taj problem rešen, vozilo mora da se nosi sa radovima na putu. Ovde su čunjevi sa leve strane koji ga primoravaju da ide na desnu ali ne samo sa radovima, naravno. Mora da se nosi sa ostalim ljudima koji se kreću oko tih radova, takođe. I, naravno, ako neko krši pravila, policija je tu i auto mora da prepozna rotaciono svetlo na krovu auta koje znači da to nije običan auto, nego policijac. Slično, ova narandžasta kutija sa strane je školski autobus i time moramo, takođe, drugačije da se pozabavimo.
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.
Kada se nalazimo na putu, drugi ljudi imaju očekivanja. Tako da kada biciklista ispruži ruku to znači da očekuje od auta da mu da prednost i napravi mesta za njega da bi se prestrojio. I kada se policijski auto nalazi na putu naše vozilo mora da prepozna da to znači da stanemo, i kada nam signaliziraiju da krenemo da mi nastavimo dalje.
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.
Način na koji ovo postižemo jeste deljenjem podataka između vozila. Prvi, grub model ovoga je kada vozilo vidi radove, i daje drugom to do znanja da bi moglo da bude u pravilnoj traci kako bi izbeglo poteškoće. Ovo zapravo mnogo dublje razumemo. Mogli bismo da uzmemo sve podatke koje auto prima u toku vremena stotine hiljada pešaka, motociklista, i vozila koje se tamo nalaze i da prepoznamo kako izgledaju i to upotrebimo da zaključimo kako bi druga vozila i drugi pešaci mogli da izgledaju. I onda, još važnije, iz toga bismo mogli da izvučemo model kako očekujemo da se oni ponašaju u svetu. Ovde žuta kocka predstavlja pešaka koji prelazi ispred nas. Ovde je plava kocka biciklista i predviđamo da će da se pomeri iza automobila desno. Ovde nam biciklista ide u susret i znamo da će nastaviti da vozi po obliku puta. Ovde neko skreće desno, i ubrzo vidimo ovde nekog ko će da napravi polukružno ispred nas pa možemo da predvidimo to i da reagujemo bezbedno.
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)
Sad, sve je to dobro za ove stvari koje smo videli. Ali, naravno, dešavaće se gomila stvari koje pre toga niste videli. Pre nekoliko meseci, naša vozila su se kretala kroz Mauntin Vju i evo sa čim smo se susreli. Ovo je žena u električnim kolicima koja juri patku u krug, po putu. (Smeh) Međutim, nigde u uputstvu o ponašanju u saobraćaju ne piše kako to da rešite. Ali naša vozila su mogla to da prepoznaju, uspore i nastave bezbedno. Nećemo se nositi samo sa patkama. Vidite kako ova ptica izleće ispred nas. Auto reaguje na to. Ovde se suočavamo sa biciklistom kojeg ne biste očekivali da vidite nigde drugde osim u Mauntin Vjuu. I, naravno, moramo da se suočavamo čak i sa veoma malim vozačima. Pogledajte desno ovde kako neko iskače iz ovog kamiona ispred nas. I ovde sa leve strane, auto sa zelenom kutijom koji je odlučio da mora da skrene desno u poslednjem mogućem trenutku. Ovde, dok se prestrojavamo, auto sa naše leve strane želi isto to. A ovde vidimo auto koji jurca kroz crveno svetlo i propuštamo ga. Slično tome, ovde biciklista isto jurca kroz crveno svetlo. I, naravno, vozilo reaguje bezbedno. Takođe imamo ljude koji rade ne-znam-ni-ja-šta na putu kao ovaj koji se zaustavlja između dva samoupravljajuće automobila. Zapitate se: "O čemu ti razmišljaš?" (Smeh)
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.
Obasuo sam vas sa dosta stvari i sada ću samo na brzinu analizirati jednu od njih. Sada gledamo situaciju, opet sa biciklistom, i na dnu možete da primetite da još uvek ne možemo da ga vidimo ali auto može: to je ona mala plava kocka tamo, i to je podatak koji se očitao laserski. To nije baš tako lako da se razume pa ću ih sada uključiti da vidimo te laserske podatke pa ako ste veoma dobri u gledanju laserskih podataka, možete uočiti ove tačke na bankini ovde, a ona plava kocka je taj biciklista. Pošto je nama sada crveno svetlo, biciklisti se već upalilo žuto, i ako škiljite, možete da vidite to ovde na slici. Ali biciklista, kako vidimo, će nastaviti pravo na raskrsnici. Nama je sada zeleno svetlo, njegovo je crveno, i mi sada predviđamo da će taj bajs da pređe preko. Nažalost, ostali vozači pored nas nisu obratili dovoljno pažnje. Počeli su da se pomeraju unapred i na svu sreću, biciklista je reagovao, izbegao, i prošao kroz raskrsnicu. I krećemo.
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.
Kao što vidite, napravili smo prilično uzbudljiv napredak i u ovom trenutku smo prilično ubeđeni da će se ova tehnologija naći na tržištu. U našim simulatorima testiramo skoro 5 miliona kilometara svakog dana pa možete zamisliti kakvo iskustvo naša vozila imaju. Radujemo se što će ova tehnologija biti na putu, i mislimo da su samoupravljajući automobili pravi put u odnosu na sistem asistencije vozaču zato što je pravovremenost veoma značajna. U toku ovog mog današnjeg govora 34 ljudi je poginulo na američkim putevima.
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.
Koliko brzo možemo izneti ovu tehnologiju? Teško je to reći jer je to zaista komplikovan problem. Ovo su moja dva dečaka. Stariji ima 11 godina a to znači da će za četiri ipo godine moći da dobije vozačku dozvolu. Moj tim i ja smo posvećeni tome da se to ne desi.
Thank you.
Hvala vam.
(Laughter) (Applause) Chris Anderson: Chris, I've got a question for you.
(Smeh) (Aplauz) Kris Anderson: Kris, imam pitanje za tebe.
Chris Urmson: Sure.
Kris Urmson: Svakako.
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.
KA: Zasigurno, svest vaših automobila je prilično zapanjujuća. Na debatu između asistencije vozaču i potpuno bezvozača - Mislim, dešava se prava debata o tome sada. Pa neke kompanije, kao što je, na primer, Tesla idu putem asistencije vozaču. Ti govoriš da će to biti ćorsokak jer ne može samo unapređivanjem te putanje da se dođe do potpunog samoupravljanja u jednom trenutku i da onda vozač kaže: "Osećam da je ovo sigurno"
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.
i onda se prebaci nazad i nešto ružno se desi. KU: Tako je. To je potpuno tačno i ne možemo da kažemo da sistemi asistencije vozaču neće biti neverovatno značajni. Mogu da spasu mnogo života u međuvremenu, ali da imaju priliku da se transformišu i pomognu ljudima kao što je Stiv da zaista dođu do kraja bezbedno, da imaju mogućnost da promene naše gradove i da se otarase urbanih kratera koje zovemo parking mestima,
CA: We will be tracking your progress with huge interest. Thanks so much, Chris. CU: Thank you. (Applause)
ovo je jedini put. KA: Pratićemo vaš napredak sa velikim interesovanjem.