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.
Dakle 1885, Karl Benz izumio je automobil. Kasnije te godine, izveo ga je na prvu javnu probnu vožnju, i -- istinita priča -- zabio se u zid. Kroz zadnjih 130 godina, radili smo oko najmanje pouzdanog dijela auta, vozača. Napravili smo aute jačim. Dodali smo sigurnosne pojase, dodali smo zračne jastuke, a u zadnjem desetljeću, zapravo smo počeli činiti aute pametnijima da popravimo taj bug, 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.
Sad, danas ću vam pričati nešto malo o razlici između krpanja oko problema sa sustavima pomoći vozaču i imanja pravih posve samovozećih automobila. te što oni mogu učiniti za svijet. Također ću vam pričati malo i o našem autu i dozvoliti vam da vidite kako on vidi svijet te kako reagira i što čini, ali prvo ću malo pričati o problemu. A to je veliki problem: 1,2 milijuna ljudi je ubijeno na svjetskim cestama svake godine. Samo u Americi, 33.000 ljudi je ubijeno svake godine. Da to stavimo u perspektivu, to je jednako kao da 737 padne s neba svaki radni dan. Na neki je način nevjerojatno. Aute nam prodaju ovako, ali zapravo, ovo je kako izgleda vožnja. Je li tako? Nije sunčano, pada kiša, i želite raditi bilo što drugo, samo ne voziti. A razlog zašto je ovaj: Promet postaje gori. U Americi, od 1990 do 2010, milje proputovane vozilima su porasle 38 posto. Porasli smo za šest posto u cestama, tako da vam to nije u glavama. Promet je zbilja bitno gori nego što je bio ne tako davno.
And all of this has a very human cost. So if you take the average commute time in America, which is about 50 minutes, you multiply that by the 120 million workers we have, that turns out to be about six billion minutes wasted in commuting every day. Now, that's a big number, so let's put it in perspective. You take that six billion minutes and you divide it by the average life expectancy of a person, that turns out to be 162 lifetimes spent every day, wasted, just getting from A to B. It's unbelievable. And then, there are those of us who don't have the privilege of sitting in traffic. So this is Steve. He's an incredibly capable guy, but he just happens to be blind, and that means instead of a 30-minute drive to work in the morning, it's a two-hour ordeal of piecing together bits of public transit or asking friends and family for a ride. He doesn't have that same freedom that you and I have to get around. We should do something about that.
A sve to ima vrlo ljudsku cijenu. Pa ako uzmete prosječno vrijeme dnevne vožnje koje je pedesetak minuta, pomnožite to sa 120 milijuna radnika koliko ih imamo, ispadne da je to otprilike šest milijardi minuta potrošenih u prometu svaki dan. Sad, to je velik broj, pa ajmo ga staviti u perspektivu. Uzmete tih šest milijardi minuta i podijelite ih sa prosječnim očekivanim životnim vijekom osobe, ispadne 162 životna vijeka potrošenih svaki dan, bačenih samo na prelazak od A do B. Nevjerojatno. A potom, ima ih među nama koji nemaju povlasticu sudjelovanja u prometu. Dakle ovo je Steve. On je nevjerojatno sposoban tip, samo što je slijep, a to znači kako umjesto 30 minutne vožnje do posla ujutro, to je dvosatno iskušenje sastavljanja djelića javnog prijevoza ili molba prijateljima i obitelji za prijevoz. On nema istu slobodu kao vi i ja glede kretanja uokolo. Trebali bismo učiniti nešto u vezi toga.
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.
Sad, uobičajena bi mudrost rekla neka samo uzmemo te sustave pomoći vozaču pa ćemo ih onda gurati i postepeno usavršavati te će se tijekom vremena premetnuti u samovozeće aute. Dobro, ovdje sam kako bih vam rekao da je to nalik izjavi kako ću ako jako uporno radim na skakanju, jednoga dana moći letjeti. Zapravo trebamo napraviti nešto malo drugačije. Pa ću vam pričati o tri različita načina na koji su samovozeći sustavi drugačiji od sustava pomoći vozaču. A započeti ću sa nekim od naših vlastitih 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.
Dakle natrag u 2013. imali smo prvi ispit samovozećeg auta gdje smo ga prepustili na korištenje običnim ljudima. Pa, gotovo običnim -- bilo je to 100 Googlovaca, ali nisu radili na projektu. Dali smo im auto i dopustili im koristiti ga u svakodnevnom životu. Ali za razliku od pravog samovozećeg auta, ovaj je dolazio sa velikom zvjezdicom: Morali su obraćati pažnju, stoga što je ovo bilo pokusno vozilo. Puno smo ga iskušavali, ali i dalje je mogao iznevjeriti. Pa smo im dali dva sata obuke, smjestili u auto, dali im koristiti ga, a što smo čuli zauzvrat je bilo nešto odlično, nekome tko pokušava donijeti proizvod na svijet. Svaki od njih nam je rekao kako ga vole. Zapravo, imalo smo vozača Poršea koji je došao i rekao nam prvi dan: "Ovo je skroz glupo. Što nam pada na pamet?" Ali na kraju, rekao je: "Ne samo da bih ga ja trebao imati, svi bi ga drugi trebali imati, jer ljudi su užasni vozači." To je bila muzika za naše uši, ali tada smo počeli gledati što su ljudi u autu radili, i to nam je otvorilo oči. Sad, moja je omiljena priča ovaj gospodin koji gleda svoj telefon i vidi da mu je baterija slaba, pa se okreće ovako u autu i kopa okolo po svojoj naprtnjači, vadi svoj laptop, stavlja ga na sjedište, ide nazad ponovo,, kopa okolo, vadi kabel za napajanje telefona, raspliće ga, ukapča ga u laptop, ukapča ga u telefon. Sigurno, telefon se puni. Svo je to vrijeme vozio 100 km na sat po autocesti. Jel tako? Nevjerojatno. Porazmislili smo o ovome i rekosmo, zapravo je na neki način očito, ne? Što će tehnologija postajati bolja, to će manje pouzdan postajati vozač. Tako da samo praveći aute postepeno pametnijima, vjerojatno nećemo vidjeti pobjede koje zbilja trebamo.
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.
Dajte da malo pričam o nečemu malo tehničkom na trenutak. Dakle gledamo ovaj grafikon, a po njegovom dnu je koliko često auto koči kada ne bi trebao. Možete ignorirati većinu te osi, jer ako vozite po gradu, a auto se počne nasumično zaustavljati, nikad nećete kupiti takav auto. A vertikalna je os koliko će često auto pritisnuti kočnicu kada bi i trebao kako bi vam pomogao izbjeći nezgodu. Sad, ako pogledamo u donji lijevi ugao, ovo je vaš klasični auto. Ne pritišće kočnice umjesto vas, ne čini ništa šašavo, ali vas također niti ne izvlači iz nezgoda. Sad, ako želimo dovesti sustav za pomoć vozaču u auto, recimo kroz kočenje radi izbjegavanja sudara, ubacit ćemo u njega neki paket tehnologije, a to je ova krivulja, i imat će neka operativna svojstva, ali nikad neće izbjeći baš sve nezgode, jer nema te sposobnosti. Ali odabrat ćemo neko mjesto na ovoj krivulji, te možda izbjegava polovicu nezgoda koje čovjek ne bi, i to je zapanjujuće, ne? Upravo smo smanjili nezgode na našim cestama za duplo. Sad 17.000 manje ljudi umire svake godine u Americi.
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 samovozeći auto, trebamo tehnološku krivulju koja izgleda ovako. Morat ćemo stavljati više senzora u vozilo, i odabrat ćemo neku operativnu točku ovdje gdje zapravo nikad ne dolazi do sudara. Događat će se, ali vrlo rijetko. Sad bismo vi i ja mogli gledati ovo i raspravljati Raste li postupno, a ja bih mogao spomenuti nešto poput pravila 80-20, a zbilja je teško popeti se do te nove krivulje. Ali pogledajmo na to iz drugog smjera na trenutak. Pogledajmo koliko često tehnologija mora učiniti pravu stvar. Tako je ova zelena točka gore sustav pomoći vozaču. Ispada da ljudski vozači čine greške koje dovode do prometnih nesreća otprilike jednom svakih 100.000 milja u Americi. Za usporedbu, samovozeći sustav vjerojatno donosi odluke oko 10 puta po sekundi, dakle red veličina, to je oko 1000 puta po milji. Pa ako usporedite udaljenost između to dvoje, to je otprilike 10^8, jel tako? Osam redova veličine. To je kao usporediti koliko brzo trčim sa brzinom svjetlosti. Nema veze koliko teško treniram, nikad zbilja neću stići tamo. Dakle tu je poprilično velik jaz.
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 konačno, tu je i kako se sustav može nositi sa nesigurnošću. Primjerice ovaj pješak će možda stati na cestu, a možda i neće. Ne mogu reći, niti to može ikoji od naših algoritama, ali u slučaju sustava pomoći vozaču, to znači kako ne može poduzeti akciju, jer ponovo ako stisne kočnicu neočekivano, to je posve neprihvatljivo. Dok samovozeći sustav može osmotriti pješaka i reći, Ne znam što se sprema učiniti, uspori, bolje osmotri, a tada se ponesi prikladno.
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.
Dakle može biti puno sigurniji nego što sustav pomoći vozaču može biti ikad . No to je dovoljno o razlikama između to dvoje. Hajdemo potrošiti neko vrijeme pričajući o tome kako auto vidi svijet.
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. Počinje od razumijevanja gdje se nalazi u svijetu, uzimajući mapu i svoje podatke iz senzora te ih usklađuje a potom stavljamo povrh toga ono što vidi u trenutku. Pa ovdje, sve ljubičaste kutije koje možete vidjeti su druga vozila. A crvena stvar tamo sa strane je biciklist, a gore u daljini, ako gledate zbilja pažljivo, možete vidjeti neke čunjiće. Tada znamo gdje se auto nalazi u nekom trenutku, Ali moramo napraviti bolje od tog: moramo predvidjeti što će se dogoditi. Pa se ovdje auto gore desno baš sprema prestrojiti u traku lijevo jer je cesta ispred njega zatvorena, pa se treba maknuti s puta. Znati o tom jednom autu je odlično, ali mi zapravo trebamo znati što svi razmišljaju, pa to postaje priično složen problem. A potom bismo mogli shvatiti kako bi auto trebao odgovarati u trenutku, dakle koju putanju bi trebao slijediti, koliko bi trebao usporiti ili ubrzati. A potom se to sve svodi samo na slijeđenje uputa: okretanje volana lijevo ili desno, pritiskanje gasa ili kočnice. To su zapravo samo dva broja na kraju dana. Pa koliko to teško zapravo može biti?
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.
Kad smo tek počinjali 2009. ovo je kako je naš sustav izgledao. Možete vidjeti naš auto u sredini te druge kutije na cesti, kako se voze autoputom. Auto mora razumjeti gdje je te ugrubo gdje su ostala vozila. To je zapravo geometrijsko razumijevanje svijeta. Jednom kad smo krenuli voziti po ulicama susjedstva i grada, problem doseže posve novu razinu teškoće. Vidite pješake kako prolaze ispred nas, aute kako prolaze ispred nas, u svakakvim smjerovima, semafore, pješačke prijelaze. To je nevjerojatno složen problem u usporedbi. A onda jednom kad taj problem imate riješen, Vozilo mora biti u stanju nositi se sa radovima na cesti Pa su ovdje čunjići s lijeva koji ga prisiljavaju na vožnju po desnoj strani, ali ne samo radovi na cesti u izolaciji, naravno. Mora se nositi i sa drugim ljudima koji se kreću kroz tu zonu radova. Te naravno, ako netko krši pravila, postoji policija a auto mora razumjeti kako rotirka na krovu tog auta znači kako to nije samo auto, već zapravo policijski dužnosnik. Slično tome, narančasta kutija tu sa strane, je školski autobus, i njega također trebamo tretirati drugačije.
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.
Kad izađemo na cestu, drugi ljudi imaju očekivanja: tako, kad biciklist ispruži ruku, to znači kako očekuju da ih auto propusti i napravi im mjesta kako bi promijenili traku. A kad policajac stoji na cesti, naš bi auto trebao razumjeti kako to znači zaustavljanje, a kad nam signaliziraju pokret, trebali bismo nastaviti.
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.
Sad, način na koji to postižemo je dijeleći podatke među vozilima. Prvi, najsiroviji model toga je kad jedno vozilo vidi zonu radova na cesti, da obavijesti drugo kako bi to znalo biti u pravoj traci kako bi izbjeglo poteškoće. Ali mi zapravo imamo puno dublje razumijevanje ovoga. Mogli bismo uzeti sve podatke koje su auti prikupili tijekom vremena stotine tisuća pješaka, biciklista, i vozila koja su bila tamo te razumjeti kako izgledaju a potom to iskoristiti kako bi zaključili kako bi druga vozila trebala izgledati i kako bi trebali izgledati drugi pješaci. A tad, čak i važnije, mogli bismo iz toga izvesti model toga kako od njih očekujemo da se kreću kroz svijet. Tako je ovdje žuta kutija pješak koji prelazi cestu ispred nas. Ovdje je plava kutija biciklist a mi očekujemo da će se progurati van i oko auta s desne strane. Ovdje imamo biciklista koji se kreće cestom a mi znamo kako će se nastaviti kretati slijedeći oblik ceste. Ovdje netko skreće desno, a za trenutak ovdje, netko će skrenuti polukružno ispred nas, i mi možemo predvidjeti to ponašanje te mu odgovoriti sigurno.
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 lijepo i krasno za stvari koje smo vidjeli, ali naravno, srećete puno stvari koje niste ranije vidjeli u svijetu. I tako baš prije par mjeseci, naša su vozila bila vozila kroz Mountain View, a ovo je što smo susreli. Ovo je žena u električnim kolicima koja ganja patku u krugovima po cesti. (Smijeh) Ispada kako nigdje u priručniku za vožnju ne piše kako se nositi s time, ali naša su vozila bila u stanju nabasati na to, usporiti, te voziti sigurno. Sad, ne moramo raditi samo sa patkama. Pogledajte ovu pticu kako prolijeće ispred nas. Auto reagira na to. Ovdje imamo posla s biciklistom kojeg ne biste očekivali vidjeti nigdje drugdje nego u Mountain Viewu. Te naravno, imamo posla i sa biciklistima, čak i vrlo malenima. Gledajte desno dok netko iskače iz kamiona točno pred nas a sad, gledajte lijevo dok auto sa zelenom kutijom odlučuje kako mora skrenuti desno u zadnji mogući trenutak. Ovdje, dok mijenjamo trake auto nama slijeva odlučuje kako želi to isto. A ovdje, gledamo auto kako prolazi kroz crveno te potom u tome ustraje. A također, ovdje, biciklist također prolazi kroz to svjetlo. Te naravno, vozilo odgovara sigurno. Te naravno, imamo ljude koji čine ne znam što ponekad na cesti, poput ovog lika koji radi škarice između dva samovozeća auta. Morate se zapitati: "što im je u glavi?" (Smijeh)
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.
Sad, zatrpao sam vas ovdje sa puno toga, Pa ću preći preko slijedećeg poprilično brzo, dakle ovdje vidimo scenu sa biciklistom ponovno, a mogli biste primijetiti na dnu, mi zapravo još ne vidimo biciklista Ali auto može: to je ta malena plava kutija tamo, a to dolazi od laserskih podataka. A to zapravo baš i nije jednostavno shvatiti, pa je ono što ću učiniti je uključiti te podatke i pogledati ih, a ako ste zbilja dobri sa gledanjem u laserske podatke, možete vidjeti nekoliko točaka na krivulji ovdje, točno ovdje, a ta je plava kutija taj biciklist. sad kako je naše svjetlo crveno, biciklistu se već upalilo žuto. A ako zaškiljite, možete to i vidjeti u slikama. Ali biciklist, vidimo, će nastaviti kroz križanje. Nama se sada upalilo zeleno, njegovo je čisto crveno, te mi sad predviđamo kako će taj bicikl proći sasvim preko križanja. Na nesreću ostali vozači pored nas ne obraćaju baš toliko pažnje. Počinju se kretati, te na sreću za sve, biciklist reagira, izbjegava, te prolazi kroz križanje. I eto ga.
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.
Sad, kako možete vidjeti, napravili smo prilično uzbudljiv napredak, te smo u ovom trenutku prilično uvjereni kako će ova tehnologija dospjeti na tržište. Radimo tri milijuna milja testova u našim simulatorima svakog dana, pa možete zamisliti iskustvo koje naša vozila imaju. Radujemo se imati ovu tehnologiju na cesti, te mislim kako ispravan put vodi kroz samovozeći prije nego kroz sustav pomoći vozaču jer žurba je toliko velika. U vremenu u kojem sam danas održao ovaj govor, 34 ljudi je poginulo na američkim cestama.
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 ovo objelodaniti? Pa, teško je reći stoga što je to zbilja složen problem, ali ovo su moja dva klinca. Starijem je 11, a to znači kako će za četiri i pol godine, biti u mogućnosti steći vlastitu vozačku dozvolu. Moj tim i ja smo predani osigurati da se to ne dogodi.
Thank you.
Hvala vam.
(Laughter) (Applause) Chris Anderson: Chris, I've got a question for you.
(Smijeh) (Pljesak) Chris Anderson: Chris, imam pitanje za tebe.
Chris Urmson: Sure.
Chris Urmson: Naravno.
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.
CA: Sigurno, um tvojih autiju je poprilično zapanjujuć. U ovoj debati između pomoći vozaču i posve bez vozača -- Mislim, postoji prava debata koja se odvija upravo sada. Dakle neke kompanije, na primjer, Tesla, idu putem pomoći vozaču. Što nam govoriš je kako će to na neki način biti slijepa ulica stoga što ne možeš samo poboljšavati po tom putu i doći do rješenja posve bez vozača u nekom trenutku, te će onda vozač reći: "Ovo ulijeva sigurnost" i zavaliti se u naslon, a tad će se dogoditi nešto ružno.
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.
CU: Tako je. Ne, to je upravo to, i nije kako će sustavi pomoći vozaču biti od nevjerojatne vrijednosti. Oni mogu sačuvati puno života u međurazdoblju, ali za vidjeti preobražajne prilike za pomoć nekome poput Stevea da se kreće, za stvarno doći do završetka priče o sigurnosti, za imati priliku promijeniti naše gradove i izbaciti parkirana vozila te se riješiti urbanih kratera - parkirališta, to je jedini pravi put.
CA: We will be tracking your progress with huge interest. Thanks so much, Chris. CU: Thank you. (Applause)
CA: Pratit ćemo vaš napredak s ogromnim zanimanjem. Hvala puno, Chris. CU: Hvala! (Pljesak)