#Google s self-driving car. Human beings make terrible drivers. They talk on the phone and run red lights, signal to the left and turn to the right. They drink too much beer and plow into trees or veer into traffic as they swat at their kids. They have blind spots, leg cramps, seizures, and heart attacks. They rubberneck, hotdog, and take pity on turtles, cause fender benders, pileups, and head-on collisions. They nod off at the wheel, wrestle with maps, fiddle with knobs, have marital spats, take the curve too late, take the curve too hard, spill coffee in their laps, and flip over their cars. Of the ten million accidents that Americans are in every year, nine and a half million are their own damn fault. A case in point: The driver in the lane to my right. He s twisted halfway around in his seat, taking a picture of the Lexus that I m riding in with an engineer named Anthony Levandowski. Both cars are heading south on Highway 880 in Oakland, going more than seventy miles an hour, yet the man takes his time. He holds his phone up to the window with both hands until the car is framed just so. Then he snaps the picture, checks it onscreen, and taps out a lengthy text message with his thumbs. By the time he puts his hands back on the wheel and glances up at the road half a minute has passed. Levandowski shakes his head. He s used to this sort of thing. His Lexus is what you might call a custom model. It s surmounted by a spinning laser turret and knobbed with cameras, radar, antennas, and G. P. S. It looks a little like an ice-cream truck, lightly weaponized for inner-city work. Levandowski used to tell people that the car was designed to chase tornadoes or to track mosquitoes, or that he belonged to an lite team of ghost hunters. But nowadays the vehicle is marked clearly:##oeself-Driving Car.##Every week for the past year and a half, Levandowski has taken the Lexus on the same slightly surreal commute. He leaves his house in Berkeley at around eight o clock, waves goodbye to his fianc e and their son, and drives to his office in Mountain view, forty-three miles away. The ride takes him over surface streets and freeways, old salt flats and pine-green foothills, across the gusty blue of San francisco bay, and down into the heart of Silicon valley. In rush-hour traffic, it can take two hours, but Levandowski doesn t mind. He thinks of it as research. While other drivers are gawking at him he is observing them: recording their maneuvers in his car s sensor logs, analyzing traffic flow, and flagging any problems for future review. The only tiresome part is when there s roadwork or an accident ahead and the Lexus insists that he take the wheel. A chime sounds, pleasant yet insistent, then a warning appears on his dashboard screen:##oein one mile, prepare to resume manual control.##Levandowski is an engineer at Google X, the company s semi-secret lab for experimental technology. He turned thirty-three last March but still has the spindly build and nerdy good nature of the kids in my high-school science club. He wears black frame glasses and oversized neon sneakers, has a long, loping stride#e s six feet seven#nd is given to excitable talk on fantastical themes. Cybernetic dolphins! Self-harvesting farms! Like a lot of his colleagues in Mountain view, Levandowski is equal parts idealist and voracious capitalist. He wants to fix the world and make a fortune doing it. He comes by these impulses honestly: his mother is a French diplomat, his father an American businessman. Although Levandowski spent most of his childhood in Brussels, his English has no accent aside from a certain absence of inflection#he bright, electric chatter of a processor in overdrive.##oemy fianc e is a dancer in her soul, #he told me.##oei m a robot.##What separates Levandowski from the nerds I knew is this: his wacky ideas tend to come true.##oei only do cool shit, #he says. As a freshman at Berkeley, he launched an intranet service out of his basement that earned him fifty thousand dollars a year. As a sophomore, he won a national robotics competition with a machine made out of Legos that could sort Monopoly money#fair analogy for what he s been doing for Google lately. He was one of the principal architects of Street view and the Google maps database, but those were just warmups.##oethe Wright Brothers era is over, #Levandowski assured me, as the Lexus took us across the Dumbarton Bridge.##oethis is more like Charles Lindbergh s plane. And we re trying to make it as robust and reliable as a 747.##Not everyone finds this prospect appealing. As a commercial for the Dodge Charger put it two years ago, #oehands-free driving, cars that park themselves, an unmanned car driven by a search-engine company? We ve seen that movie. It ends with robots harvesting our bodies for energy.##Levandowski understands the sentiment. He just has more faith in robots than most of us do.##oepeople think that we re going to pry the steering wheel from their cold, dead hands, #he told me, but they have it exactly wrong. Someday soon, he believes, a self-driving car will save your life. The Google car is an old-fashioned sort of science fiction: this year s model of last century s Make it belongs to the gleaming, chrome-plated age of jet packs and rocket ships, transporter beams and cities beneath the sea, of a predicted future still well beyond our technology. In 1939, at the World s Fair in New york, visitors stood in lines up to two miles long to see the General motors Futurama exhibit. Inside, a conveyor belt carried them high above a miniature landscape, spread out beneath a glass dome. Its suburbs and skyscrapers were laced together by superhighways full of radio-guided cars.##oedoes it seem strange? Unbelievable?##the announcer asked.##oeremember, this is the world of 1960.##Not quite. Skyscrapers and superhighways made the deadline, but driverless cars still putter along in prototype. Human beings, as it turns out, aren t easy to improve upon. For every accident they cause, they avoid a thousand others. They can weave through tight traffic and anticipate danger, gauge distance, direction, pace, and momentum. Americans drive nearly three trillion miles a year, I was told by Ron Medford, a former deputy administrator of the National Highway Traffic Safety Administration who now works for Google. It s no wonder that we have thirty-two thousand fatalities along the way he said. It s a wonder the number is so low. Levandowski keeps a collection of vintage illustrations and newsreels on his laptop, just to remind him of all the failed schemes and fizzled technologies of the past. When he showed them to me one night at his house, his face wore a crooked grin, like a father watching his son strike out in Little league. From 1957: A sedan cruises down a highway, guided by circuits in the road, while a family plays dominoes inside.##oeno traffic jam...no collisions...no driver fatigue.##From 1977: Engineers huddle around a driverless Ford on a test track.##oecars like this one may be on the nation s roads by the year 2000!##Levandowski shook his head.##oewe didn t come up with this idea, #he said.##oewe just got lucky that the computers and sensors were ready for us.##Almost from the beginning, the field divided into two rival camps: smart roads and smart cars. General motors pioneered the first approach in the late nineteen-fifties. Its Firebird III concept car#haped like a jet fighter, with titanium tail fins and a glass-bubble cockpit#as designed to run on a test track embedded with an electrical cable, like the slot on a toy speedway. As the car passed over the cable, a receiver in its front end picked up a radio signal and followed it around the curve. Engineers at Berkeley later went a step further: they spiked the track with magnets, alternating their polarity in binary patterns to send messages to the car#oeslow down, sharp curve ahead.##Systems like these were fairly simple and reliable, but they had a chicken-and-egg problem. To be had useful, they to be built on a large scale; to be built on a large scale, they had to be useful.##oewe don t have the money to fix potholes, #Levandowski says.##oewhy would we invest in putting wires in the road?##Smart cars were more flexible but also more complex. They needed sensors to guide them, computers to steer them, digital maps to follow. In the nineteen-eighties, a German engineer named Ernst Dickmanns, at the Bundeswehr University in Munich, equipped a Mercedes van with video cameras and processors, then programmed it to follow lane lines. Soon it was steering itself around a track. By 1995, Dickmanns s car was able to drive on the Autobahn from Munich to Odense, Denmark, going up to a hundred miles at a stretch without assistance. Surely the driverless age was at hand! Not yet. Smart cars were just clever enough to get drivers into trouble. The highways and test tracks they navigated were controlled strictly environments. The instant more variables were added#pedestrian, say, or a traffic cop#heir programming faltered. Ninety-eight per cent of driving is just following the dotted line. It s the other two per cent that matters.##oethere was no way, before 2000, to make something interesting, #the roboticist Sebastian Thrun told me.##oethe sensors weren t there, the computers weren t there, and the mapping wasn t there. Radar was a device on a hilltop that cost two hundred million dollars. It wasn t something you could buy at Radio shack.##Thrun, who is forty-six, is the founder of the Google Car project. A wunderkind from the west German city of Solingen, he programmed his first driving simulator at the age of twelve. Slender and tan, with clear blue eyes and a smooth, seemingly boneless gait, he looks as if he just stepped off a dance floor in Ibiza. And yet, like Levandowski, he has a gift for seeing things through a machine s eyes #or intuiting the logic by which it might apprehend the world. When Thrun first arrived in the United states, in 1995 he took a job at the country s leading center for driverless car research: Carnegie mellon University. He went on to build robots that explored mines In virginia, guided visitors through the Smithsonian, and chatted with patients at a nursing home. What he didn t build was driverless cars. Funding for private research in the field had dried up by then. And though Congress had set a goal that a third of all ground combat vehicles be autonomous by 2015, little had come of the effort. Every so often, Thrun recalls, military contractors, funded by the Defense Advanced Research Projects Agency, would roll out their latest prototype.##oethe demonstrations I saw mostly ended in crashes and breakdowns in the first half mile, #he told me.##oedarpa was funding people who weren t solving the problem. But they couldn t tell if it was the technology or the people. So they did this crazy thing, which was really visionary.##They held a race. The first darpa Grand Challenge took place in the Mojave desert on March 13, 2004. It offered a million-dollar prize for what seemed like a simple task: build a car that can drive a hundred and forty-two miles without human intervention. Ernst Dickmanns s car had gone similar distances on the Autobahn, but always with a driver in the seat to take over in the tricky stretches. The cars in the Grand Challenge would be empty, and the road would be rough: from Barstow, California, to Primm, Nevada. Instead of smooth curves and long straightaways, it had rocky climbs and hairpin turns; instead of road signs and lane lines, G. P. S. waypoints.##oetoday, we could do it in a few hours, #Thrun told me.##oebut at the time it felt like going to the moon in sneakers.##Levandowski first heard about it from his mother. She d seen a notice for the race when it was announced online, in 2002, and recalled that her son used to play with remote-control cars as a boy, crashing them into things on his bedroom floor. Was this so different? Levandowski was now a student at Berkeley, in the industrial-engineering department. When he wasn t studying or rowing crew or winning Lego competitions, he was casting about for cool new shit to build#or a profit, if possible.##oeif he s making money, it s his confirmation that he s creating value, #his friend Randy Miller told me.##oei remember, when we were in college, we were at his house one day, and he told me that he d rented out his bedroom. He d put up a wall in his living room and was sleeping on a couch in one half, next to a big server tower that he d built. I said,#Anthony, what the hell are you doing? You ve got plenty of money. Why don t you get your own place? And he said,#No. Until I can move to a stateroom on a 747, I want to live like this.##darpa s rules were vague on the subject of vehicles: anything that could drive itself would do. So Levandowski made a bold decision. He would build the world s first autonomous motorcycle. This seemed like a stroke of genius at the time. Miller says that it came to them in a hot tub in Tahoe, which sounds about right.)Good engineering is all about gaming the system, Levandowski says#bout sidestepping obstacles rather than trying to run over them. His favorite example is from a robotics contest at M. I t. in 1991. Tasked with building a machine that could shoot the most Ping-pong balls into a tube, the students came up with dozens of ingenious contraptions. The winner, though, was infuriatingly simple: it had a mechanical arm reach over, drop a ball into the tube, then cover it up so that no others could get in. It won the contest in a single move. The motorcycle could be like that, Levandowski thought: quicker off the mark than a car and more maneuverable. It could slip through tighter barriers and drive just as fast. Also, it was a good way to get back at his mother, who d never let him ride motorcycles as a kid.##oefine,#he thought.##oei ll just make one that rides itself.##The flaw in this plan was obvious: a motorcycle can t stand up on its own. It needs a rider to balance it#r else a complex, computer-controlled system of shafts and motors to adjust its position every hundredth of a second.##oebefore you can drive ten feet you have to do a year of engineering, #Levandowski says. The other racers had no such problem. They also had substantial academic and corporate backing: the Carnegie mellon team was working with General motors, Caltech with Northrop grumman, Ohio State with Oshkosh trucking. When Levandowski went to the Berkeley faculty with his idea, the reaction was bemused, at best disbelief. His adviser, Ken Goldberg, told him frankly that he had no chance of winning.##oeanthony is probably the most creative undergraduate I ve encountered in twenty years, #he told me.##oebut this was a very great stretch.##Levandowski was unfazed. Over the next two years, he made more than two hundred cold calls to potential sponsors. He gradually scraped together thirty thousand dollars from Raytheon, Advanced micro devices, and others. No motorcycle company was willing to put its name on the project. Then he added a hundred thousand dollars of his own. In the meantime, he went about poaching the faculty s graduate students.##oehe paid us in burritos,#Charles Smart, now a professor of mathematics at M. I t.,told me.##oealways the same burritos. But I remember thinking, I hope he likes me and lets me work on this.##Levandowski had that effect on people. His mad enthusiasm for the project was matched only by his technical grasp of its challenges#nd his willingness to go to any lengths to meet them. At one point, he offered Smart s girlfriend and future wife five thousand dollars to break up with him until the project was done.##oehe was fairly serious, #Smart told me.##oeshe hated the motorcycle project.##There came a day when Goldberg realized that half his Ph d. students had been working for Levandowski. They d begun with a Yamaha dirt bike, made for a child, and stripped it down to its skeleton. They added cameras, gyros, G. P. S. modules, computers, roll bars, and an electric motor to turn the wheel. They wrote tens of thousands of lines of code. The videos of their early test runs, edited together, play like a jittery reel from#oethe Benny hill Show#:#bike takes off, engineers jump up and down, bike falls over#ore than six hundred times in a row.##oewe built the bike and rebuilt the bike, just sort of groping in the dark, #Smart told me.##oeit s like one of my colleagues once said:##You don t understand, Charlie, this is robotics. Nothing actually works.##Finally, a year into the project, a Russian engineer named Alex Krasnov cracked the code. They d thought that stability was a complex, nonlinear problem, but it turned out to be fairly simple. When the bike tipped to one side, Krasnov had it steer ever so slightly in the same direction. This created centrifugal acceleration that pulled the bike upright again. By doing this over and over, tracing tiny S-curves as it went, the motorcycle could hold to a straight line. On the video clip from that day, the bike wobbles a little at first, like a baby giraffe finding its legs, then suddenly, confidently circles the field#s if guided by an invisible hand. They called it the Ghost rider. The Grand Challenge proved to be one of the more humbling events in automotive history. Its sole consolation lay in shared misery. None of the fifteen finalists made it past the first ten miles; seven broke down within a mile. Ohio State s six-wheel, thirty-thousand-pound Terramax was brought up short by some bushes; Caltech s Chevy Tahoe crashed into a fence. Even the winner, Carnegie mellon, earned at best a Pyrrhic victory. Its robotic Humvee, Sandstorm, drove just seven and a half miles before careering off course. A helicopter later found it beached on an embankment, wreathed in smoke, its back wheels spinning so furiously that they d burst into flame. As for the Ghost rider, it managed to beat out more than ninety cars in the qualifying round#mile -and-a-half obstacle course on the California Speedway in Fontana. But that was its high-water mark. On the day of the Grand Challenge standing at the starting line in Barstow, half delirious with adrenaline and fatigue, Levandowski forgot to turn on the stability program. When the gun went off, the bike sputtered forward, rolled three feet, and fell over.##oethat was a dark day, #Levandowski says. It took him a while to get over it#t least by his hyperactive standards.##oei think I took, like, four days off, #he told me.##oeand then I was like, Hey, I m not done yet! I need to go fix this!##darpaapparently had thought the same. Three months later, the agency announced a second Grand Challenge for the following October, doubling the prize money to two million dollars. To win, the teams would have to address a daunting list of failures and shortcomings, from fried hard drives to faulty satellite equipment. But the underlying issue was always the same: as Joshua Davis later wrote inwired, the robots just weren t smart enough. In the wrong light, they couldn t tell a bush from a boulder, a shadow from a solid object. They reduced the world to a giant marble maze, then got caught in the thickets between holes. They needed to raise their I q. In the early nineties, Dean Pomerleau, a roboticist at Carnegie mellon, had hit upon an unusually efficient way to do this: he let his car teach itself. Pomerleau equipped the computer in his minivan with artificial neural networks, modelled on those in the brain. As he drove around Pittsburgh, they kept track of his driving decisions, gathering statistics and formulating their own rules of the road.##oewhen we started, the car was going about two to four miles an hour along a path through a park#ou could ride a tricycle faster, #Pomerleau told me.##oeby the end, it was going fifty-five miles per hour on highways.##In 1996, the car steered itself from Washington, D c, . to San diego with only minimal intervention#early four times as far as Ernst Dickmanns s cars had gone a year earlier.##oeno Hands Across America,#Pomerleau called it. Machine learning is an idea nearly as old as computer science#lan Turing, one of the fathers of the field, considered it the essence of artificial intelligence. It s often the fastest way for a computer to learn a complex behavior, but it has its drawbacks. A self-taught car can come to some strange conclusions. It may confuse the shadow of a tree for the edge of the road, or reflected headlights for lane markers. It may decide that a bag floating across a road is a solid object and swerve to avoid it. It s like a baby in a stroller deducing the world from the faces and storefronts that flicker by. It s hard to know what it knows.##oeneural networks are like black boxes, #Pomerleau says.##oethat makes people nervous, particularly when they re controlling a two-ton vehicle.##Computers, like children, are taught more often by rote. They re given thousands of rules and bits of data to memorize#f X happens, do Y; avoid big rocks#hen sent out to test them by trial and error. This is slow, painstaking work, but it s easier to predict and refine than machine learning. The trick, as in any educational system, is to combine the two in proper measure. Too much rote learning can make for a plodding machine. Too much experiential learning can make for blind spots and caprice. The roughest roads in the Grand Challenge were often the easiest to navigate, because they had clear paths and well-defined shoulders. It was on the open, sandy trails that the cars tended to go crazy.##oeput too much intelligence into a car and it becomes creative, #Sebastian Thrun told me. The second Grand Challenge put these two approaches to the test. Nearly two hundred teams signed up for the race, but the top contenders were clear from the start: Carnegie mellon and Stanford. The C. M. U. team was led by the legendary roboticist William (Red) Whittaker. Pomerleau had left the university by then to start his own firm. A burly, mortar-headed ex-marine, Whittaker specialized in machines for remote and dangerous locations. His robots had crawled over Antarctic ice fields and active volcanoes, and inspected the damaged nuclear reactors at Three Mile Island and Chernobyl. Seconded by a brilliant young engineer named Chris Urmson, Whittaker approached the race as a military operation, best won by overwhelming force. His team spent twenty-eight days laser-scanning the Mojave to create a computer model of its topography; then they combined those scans with satellite data to help identify obstacles.##oepeople don t count those who died trying, #he later told me. The Stanford team was led by Thrun. He hadn taken t part in the first race, when he was still just a junior faculty member at C. M. U . But by the following summer he had accepted an endowed professorship in Palo alto . When darpa announced the second race, he heard about it from one of his Ph d. students, Mike Montemerlo.##oehis assessment of whether we should do it was no, but his body and his eyes and everything about him said yes,#Thrun recalls.##oeso he dragged me into it.##The contest would be a study in opposites: Thrun the suave cosmopolitan; Whittaker the blustering field marshal. Carnegie mellon with its two military vehicles, Sandstorm and Highlander; Stanford with its puny Volkswagen Touareg, nicknamed Stanley. It was an even match. Both teams used similar sensors and software, but Thrun and Montemerlo concentrated more heavily on machine learning.##oeit was our secret weapon, #Thrun told me. Rather than program the car with models of the rocks and bushes it should avoid, Thrun and Montemerlo simply drove it down the middle of a desert road. The lasers on the roof scanned the area around the car, while the camera looked farther ahead. By analyzing this data, the computer learned to identify the flat parts as road and the bumpy parts as shoulders. It also compared its camera images with its laser scans, so that it could tell what flat terrain looked like from a distance#nd therefore drive a lot faster.##oeevery day it was the same,#Thrun recalls.##oewe would go out, drive for twenty minutes, realize there was some software bug, then sit there for four hours reprogramming and try again. We did that for four months.##When they started, one out of every eight pixels that the computer labelled as an obstacle was nothing of the sort. By the time they were done, the error rate had dropped to one in fifty thousand. On the day of the race, two hours before start time, darpa sent out the G. P. S. co rdinates for the course. It was even harder than the first time: more turns, narrower lanes, three tunnels, and a mountain pass. Carnegie mellon, with two cars to Stanford s one, decided to play it safe. They had Highlander run at a fast clip#ore than twenty miles an hour on average#hile Sandstorm hung back a little. The difference was enough to cost them the race. When Highlander began to lose power because of a pinched fuel line, Stanley moved ahead. By the time it crossed the finish line, six hours and fifty-three minutes after it started, it was more than ten minutes ahead of Sandstorm and more than twenty minutes ahead of Highlander. It was a triumph of the underdog of brain over brawn. But less for Stanford than for the field as a whole. Five cars finished the hundred-and-thirty-two-mile course; more than twenty cars went farther than the winner had in 2004. In one year, they d made more progress than darpa s contractors had in twenty.##oeyou had these crazy people who didn t know how hard it was told, #Thrun me.##oethey said,#Look, I have a car, I have a computer, and I need a million bucks. So they were doing things in their home shops, putting something together that had never been done in robotics before, and some were insanely impressive.##A team of students from Palos verdes High school in California, led by a seventeen-year-old named Chris Seide, built a self-driving#oedoom Buggy#that, Thrun recalls, could change lanes and stop at stop signs. A Ford S. U. V. programmed by some insurance-company employees from Louisiana finished just thirty-seven minutes behind Stanley. Their lead programmer had lifted his preliminary algorithms from textbooks on video-game design.##oewhen you look back at that first Grand Challenge, we were in the Stone age compared to where we are told now, #Levandowski me. His motorcycle embodied that evolution. Although it never made it out of the semifinals of the second race#ripped up by some wooden boards#he Ghost rider had become, in its way, a marvel of engineering, beating out seventy-eight four-wheeled competitors. Two years later, the Smithsonian added the motorcycle to its collection; a year after that, it added Stanley as well. By then, Thrun and Levandowski were both working for Google. The driverless car project occupies a lofty, garagelike space in suburban Mountain view. It s part of a sprawling campus built by Silicon graphics in the early nineties and repurposed by Google, the conquering army, a decade later. Like a lot of high-tech offices, it s a mixture of the whimsical and the workaholic#andy-colored sheet metal over a sprung-steel chassis. There s a Foosball table in the lobby, exercise balls in the sitting room, and a row of what look like clown bicycles parked out front, free for the taking. When you walk in, the first things you notice are the wacky tchotchkes on the desks: Smurfs,#oestar Wars#toys, Rube Goldberg devices. The next things you notice are the desks: row after row after row, each with someone staring hard at a screen. It had taken me two years to gain access to this place, and then only with a staff member shadowing my every step. Google guards its secrets more jealously than most. At the gourmet cafeterias that dot the campus, signs warn against#oetailgaters##orporate spies who might slink in behind an employee before the door swings shut. Once inside, though, the atmosphere shifts from vigilance to an almost missionary zeal.##oewe want to fundamentally change the world with this,#Sergey Brin, the cofounder of Google, told me. Brin was dressed in a charcoal hoodie, baggy pants, and sneakers. His scruffy beard and flat, piercing gaze gave him a Rasputinish quality, dulled somewhat by his Google glass eyewear. At one point, he asked if I d like to try the glasses on. When I d positioned the miniature projector in front of my right eye, a single line of text floated poignantly into view:##oe3: 51 p m. It s okay.####oeas you look outside, and walk through parking lots and past multilane roads, the transportation infrastructure dominates, #Brin said.##oeit s a huge tax on the land.##Most cars are used only for an hour or two a day, he said. The rest of the time, they re parked on the street or in driveways and garages. But if cars could drive themselves, there would be need no for most people to own them. A fleet of vehicles could operate as a personalized public-transportation system, picking people up and dropping them off independently, waiting at parking lots between calls. They d be cheaper and more efficient than taxis#y some calculations, they d use half the fuel and a fifth the road space of ordinary cars#nd far more flexible than buses or subways. Streets would clear highways shrink, parking lots turn to parkland.##oewe re not trying to fit into an existing business model, #Brin said.##oewe are just on such a different planet.##When Thrun and Levandowski first came to Google, in 2007, they were given a simpler task: to create a virtual map of the country. The idea came from Larry page, the company s other cofounder. Five years earlier, Page had strapped a video camera on his car and taken several hours of footage around the Bay Area. He d then sent it to Marc Levoy, a computer-graphics expert at Stanford, who created a program that could paste such footage together to show an entire streetscape. Google engineers went on to jury-rig some vans with G. P. S . and rooftop cameras that could shoot in every direction. Eventually, they were able to launch a system that could show three hundred-and-sixty-degree panoramas for any address. But the equipment was unreliable. When Thrun and Levandowski came on board they helped the team retool and reprogram. Then they equipped a hundred cars and sent them all over the United states. Google street view has since spread to more than a hundred countries. It s both a practical tool and a kind of magic trick#spyglass onto distant worlds. To Levandowski, though, it was just a start. The same data, he argued, could be used to make digital maps more accurate than those based on G. P. S. data, which Google had been leasing from companies like navteq. The street and exit names could be drawn straight from photographs for instance, rather than faulty government records. This sounded simple enough but proved to be complicated fiendishly. Street view mostly covered urban areas, but Google maps had to be comprehensive: every logging road logged on a computer, every gravel drive driven down. Over the next two years, Levandowski shuttled back and forth to Hyderabad, India, to train more than two thousand data processors to create new maps and fix old ones. When Apple s new mapping software failed so spectacularly a year ago, he knew exactly why. By then, his team had spent five years entering several million corrections a day. Street view and Maps were logical extensions of a Google search. They showed you where to locate the things you d found. What was missing was a way to get there. Thrun despite his victory in the second Grand Challenge, didn t think that driverless cars could work on surface streets#here were just too many variables.##oei would have told you then that there is no way on earth we can drive safely, #he says.##oeall of us were in denial that this could be done.##Then, in February of 2008, Levandowski got a call from a producer of#oeprototype This!##a series on the Discovery Channel. Would he be interested in building a self-driving pizza delivery car? Within five weeks, he and a team of fellow Berkeley graduates and other engineers had re

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