Chapter 1: What sparked the interest in driverless cars?
PJ, how have you been? I've been good. How have you been? Yeah, I'm a little better now having listened to your series. I love it. Oh, thank you. Do you recognize that voice? It is PJ Vogt, host of the podcast Search Engine and friend of Freakonomics Radio.
You may remember hearing him back in 2024 when we published a Search Engine episode called The Fascinatingly Mundane Secrets of the World's Most Exclusive Nightclub about Berghain in Berlin. That was a great story. And not too long ago, PJ came to us with another one. It's a two-part series on driverless cars.
This is a topic that we have touched on many times over the years at Freakonomics Radio, but PJ decided to go deep. The other day, I had a chance to ask him how he got interested in this. There's a whole lesson in this, but I'd gotten, and this is not the next sentence you're going to expect me to say, too into bench pressing.
Yeah.
That's not where I thought you were going. And I injured myself. I had a hernia and then I had to have a hernia repair. I see. So there were like some minor complications. I was not moving easily. I was in a lot of pain. So I had kind of limited mobility and I was visiting a friend in San Francisco and I took a Waymo and it was such an experience of the future that immediately becomes normal.
First, the idea that I would press a button on my phone, a Car would come out of nowhere, driven by nobody. I would get in, watch the steering wheel turn itself. I was trying to describe to somebody recently, I was like, the first time it feels like the first time you're in an airplane, and by the third time it feels like you're in an elevator.
It was a moment where I thought, oh, a lot's about to change. And it was confusing to me that people were not talking about that more. What should we expect to hear in the series? There are two parts. The first is really about the car, and then the second is really about the driver. Tell me who you think are some of the most compelling characters and why.
So in the first part, there's this guy, Sebastian Thrun. He's so good. He's this German-born roboticist AI expert who lost a friend as a teenager to a car accident. And he really thinks that his invention is not just going to make money for a tech company or be more convenient. He wants to reshape the modern world as it exists.
And it's just the story of him and his team beginning to figure that out and having ideas that sounded crazy 20 years ago and with every year towards the present have sounded more sane and at least plausible. And then in the second part, I find the Boston politicians to be very vivid talkers, very opinionated people. Vivid is a very polite word. They're strongly opinionated.
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Chapter 2: How did DARPA's Grand Challenge influence autonomous vehicle technology?
That's the knocker-upper, here to get you up for work. We're in the 1800s, before the invention of the adjustable alarm clock. The knocker-upper is a job. The knocker-upper walks the neighborhood with a long stick and taps it on the windows of people's houses early in the morning to wake them up for work. Who wakes up the knocker-upper for work? Nobody knows.
But this is a job, a job that'll actually exist for another century. Outside, the gas street lamps are still burning. The lamplighter lit them the night before. He's supposed to come at dawn to extinguish them, but it's so early that he hasn't yet. Your lamplighter is one of those neighbors you have a deep fondness for, a fixture.
Every day, you watch him make the rounds at dusk with his ladder and his light. You yourself are a driver. Professional driver, 200 years ago, is also a job. You're a person who sits on a coach and holds the reins of a horse. You take passengers where they wanna go. You start your work day. Okay, hypothetical over.
Two of those jobs are obviously so long disappeared that most people don't know about them. The knocker upper is your iPhone alarm. The lamplighter is the electric streetlight. The third one, driver, has persisted, as a job for some, as a routine human task for nearly everyone else. This is a story about whether that's about to change.
It's about how the word driver, which right now makes me picture a human, could soon transform to refer to a machine, the same way the words dishwasher, printer, and computer all did. I've thought about this maybe too much in the year I've been working on this story. In conversations, constantly, I'd ask the humans the same question. Are you a good driver? Do you consider yourself a good driver?
I do, within limits. I think I'm a good driver because I understand the limitations of my driving. This is Alex Davies. He wrote an excellent book called Driven, The Race to Create the Autonomous Car. Alex, like me, thinks a lot about human driving, about his own personal limitations. What are the limitations? The limitations are that I can't always pay attention to everything, that I get tired.
I've been trying really hard to be calmer on the road. My husband and I are expecting our first baby this fall. Congratulations. Thank you. And I thought that along with like reading all the baby books, a good project to work on is just be calmer in the car. A very good resolution, because of course, for most of us, driving is the riskiest behavior we routinely engage in.
In fact, even Alex, despite his good intentions, would actually get in a car accident just a few months after we first spoke. He was okay. It was the car that was totaled. Safety is the entire pitch for the driverless car, which is really a car driven by a computer.
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Chapter 3: What were the challenges faced during the first Grand Challenge?
Driverless cars don't get drunk, tired, or distracted. They never text or feel road rage. And these driverless cars, they aren't the future. They're actually already here. But it's funny, if you just don't happen to live in a place that already has them, it's easy to not see how fast things are changing.
Robo-taxis like Waymo are operating in 10 American cities, providing millions of rides to Americans. In China, the rollout is happening even more widely. They're in twice as many cities. But here, if you live in a place like San Francisco or Austin, today a driverless car is about as exotic as an Uber.
A passenger in those cities opens up their phone and decides who should drive them, a human driver or a robot driver. How that happened is a story, a story we are living through right now, whose ending promises to totally reshape the places we live. And today we're gonna tell you how we got here in chapters. Chapter one, dreams without drivers.
So it turns out this dream that inventors have had to replace the human driver with some kind of machine, that dream is about as old as the lamplighters. People have been thinking about a self-driving car for just about as long as there's been a human-driven car. Why?
There's this funny thing you lose when you move from the horse to the human-driven car, which is that in a horse-drawn carriage, the horse is not just going to run off a cliff if you let go of the reins. You lose sentience in your vehicles. When automobiles first arrived, these powerful and non-sentient cars, there was actually a passionate fight to keep them off the streets.
It was the 1800s, and people feared these new things. The steam-powered vehicles thundering down the roads that soon evolved into gas-powered vehicles also thundering down the roads. The fear was partly about jobs. These vehicles were seen as a huge threat to a whole network of working class jobs.
Horse breeders and horse farriers, horse feed suppliers, horse manure haulers, horse carriage manufacturers, not to mention the teamsters. Teamsters, today the word makes me think of the teamsters union, but originally the teamsters were the workers who drove teams of horses. Teamsters were like truckers before we had trucks. Cars seemed to imperil all these horse-related jobs.
And even if you weren't worried about these workers, the cars were also less safe. Some anti-car activists battled to stop or slow the new technology, mainly with regulations. There were red flag laws, which said if you had an automobile, you had to hire a person to walk in front of it, waving a giant red flag to warn people.
In Pennsylvania, a law was proposed requiring horseless carriage drivers who encountered livestock to stop, disassemble their car, and hide the parts behind the bushes. The governor vetoed it. But the thing about these crazy anti-car activists is that directionally, they were right. Those cars did initially wipe out a lot of jobs, even if they created more. And cars were very unsafe.
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Chapter 4: How did Google start its driverless car project?
So that thought is there early, and early visions of it include, oh, well, we'll have radio-controlled cars because they had radios at the time. There's a real effort at one point to build magnets under the road. And at each stage, what a self-driving car can be is dictated by the technology that's available at the time, for the most part. No one's thinking that much about...
a vehicle that thinks for itself. They're just thinking about a vehicle that the person in it doesn't have to drive. Many different attempts, many different failures. As many wonders as we invented, we could not approach nature's most majestic creation, a horse's brain. At least not until the turn of the millennium.
Nine, eight, seven, six, five, four, three, two, one.
Deep within the Department of Defense, there's a little-known military agency that has created some of the most innovative technology of the 20th century. This is the story of DARPA. Chapter 2, DARPA's Million Dollar Prize. DARPA's current goal is to develop autonomous military vehicles, machines that can operate on their own, without drivers.
DARPA's always been intrigued with...
This is from a documentary called The Million Dollar Challenge. Honestly, less a doc, more an ad for DARPA, the Pentagon's research arm. DARPA's mission is to try to keep American technology one generation ahead of everybody else. It doesn't always work, but DARPA has invented or funded a lot. GPS and the M16, thoroughly internet, and the Predator drone.
In 2002, DARPA decided to pursue the driverless car in a very unusual way. The director of DARPA at the time, a guy named Tony Tether, who had been a door-to-door salesman in his youth, definitely has that flair and that way of thinking, says, let's have a contest. Let's see who can put all of these ingredients that we've developed together into a proper self-driving car.
His original idea is, we'll drive him down the Las Vegas Strip. That's almost immediately next because it's insane. Oh, right. You would have to literally gridlock a huge American city so people could put robot cars on it. Exactly. So he says, okay, do you know what? We'll do it in the desert. We'll do it in the desert outside Las Vegas.
And anyone who wants to can make a team, build a self-driving car, bring it to the desert, and we'll race him. The driver that DARPA wanted to replace was the American soldier. DARPA wanted a vehicle that could drive itself down roads that might be filled with hidden explosive devices.
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Chapter 5: What advancements did Waymo achieve in autonomous driving?
was still rather primitive, especially compared to what we have today. But he could use it to teach his vehicle how to recognize the road and how to do it much faster. They found a dirt road out near Stanford and they drive it down a dirt road and have the car's cameras record what they were seeing. the robot Stanley was able to train itself as it ran.
And the way it worked is its eyes looked way ahead and it could see stuff way at a distance. When it drives over the stuff, it could tell if it was a good place to drive or not because it could measure how slippery or how bumpy the road was. And then it could then retroactively train itself and say, this green stuff over there, it's something good to drive on, aka grass.
And this brownish stuff, aka mud, is not so good to drive. And so it was able to detect patterns and generalize from what it had learned? Yeah, absolutely. And it did this like 30 times a second. I mean, just like a person. The race kicks off with Stanley sandwiched between Carnegie Mellon's two behemoths. Highlander leads the pack, followed by Stanley and Sandstorm.
What happens in the second race? The second race is as successful as the first race is disastrous. Nearly every entrant in the second race would go further than Sandstorm had in the first. Multiple vehicles would finish the course. The real question was who would do it fastest. And so at what point was it clear to you that you were going to win?
Chapter 6: How does Waymo's safety data compare to human drivers?
Well, once we passed the front running team, we kind of saw the vehicle descend into what was the hardest part of the race course, a very, very treachery mountain pass. And we saw at a distance a dust cloud. We saw a helicopter. We saw little features that made us believe, wow, there's something happening that's magical.
And this dust cloud then all of a sudden turned bluish because the car was blue and came closer. And then it came first to the finish line. It was unbelievably magical. At the end of the doc, over some criminally corny piano music, Sebastian Thrun gives his post-race interview. He's dressed a lot like a race car driver, watching You Could Forget He Wasn't In The Car.
It was just amazing to see this community of people.
That community succeeded today. Behind me, there are three robots that made it all the way through the desert, and all three of them did the unthinkable. It's such a fantastic success for this community. I think we all win.
a made-for-TV kumbaya moment, still years before the race to build driverless cars would enter its cutthroat phase. What would happen next is that a small band of lunatics would take driverless cars out of the desert, start secretly driving them on public roads in the state of California.
They would do this at the behest of a man who had been observing from the stands that day, disguised in a hat and sunglasses, who'd watched the challenge while his mind spun. That's after a short break. I'm Stephen Dubner, and you are listening to a special episode of the podcast Search Engine here on Freakonomics Radio. We will be right back. Hey there, it's Stephen Dubner.
Today we are running an episode from the Search Engine podcast with host PJ Vogt. Chapter four, something actually useful for the world. The race in the desert had been designed as a spectacle, something flashy to draw out America's smartest roboticists. But it had drawn another person who'd come for his own reasons.
Google's Larry Page arrived at the DARPA Grand Challenge in a baseball hat and sunglasses, a disguise. He found Sebastian Thrun and buttonholed him, asking him a million highly specific questions about things like the wavelength his LIDAR system used. But this meeting in the desert, this was not actually their first introduction. Well, the first time I met Larry was a bit earlier.
He had built a small little robot that acted as a telepresence for meetings. And he was trying to drive it around the Google offices instead of himself going to meeting with a robot. And he sent me a message and said, I'm going to show you the robot I've built.
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Chapter 7: What controversies have arisen in the development of self-driving cars?
And Larry would come back the next day with the same idea, and I would give him the same answer. And both of us got increasingly more frustrated. Like, God damn it, it can't be done. And eventually he came and said, look, Sebastian, okay, I get it. You can't do it. I want to explain to Eric Schmidt, the CEO at the time, and Sergey Brin, my co-founder, why it can't be done.
Can you give me the technical reason why it can't be done? And that's the moment of incredible pain because I go home and I can't think of a technical reason why not. It was this kind of moment where I felt, look, I'm the world expert on self-driving cars and I'm the person who denies that it can be done. Like that taught me an incredibly important lesson about experts.
that for the rest of my life, I decided experts are usually experts of the past, not the future. And if you ask an expert about innovation, something crazy new, they're the least likely person to say, yes, it can be done. So this is where the Google self-driving car project begins in 2009. It's led by Sebastian, joined by others from the DARPA challenges.
The methodical Chris Armisen was running most things day to day. Anthony Lewandowski, the flashy motorcycle guy, would work on hardware. Dmitry Dolgov, another DARPA veteran, would be responsible for planning and optimization. It was a secret project. They'd report directly to Larry Page, a small enough team that there'd be no bureaucracy, few emails, fewer meetings.
Just 11 engineers who writer Alex Davies says represented some of the best young talent in the country. And so Google builds this very quiet team, and it says to them, build us a self-driving car. And because that goal is super nebulous, they give them two challenges. They say, safely log 100,000 miles on public roads, but they also give them a challenge called the Larry 1K.
So Larry and Sergey and I sat together and the two of them carved out a thousand total miles of road surface in California. They open up Google Maps and they just click around and they look for 10 separate 100-mile routes that are really tricky. Absolutely everything, like the Bay Bridge and Lake Tahoe and Highway 1 to Los Angeles and Market Street and even Crooked Lombard Street.
And they say to the team, you have to drive each of these 100-mile routes without one human takeover of the system, without one failure of the car. To get off to a running start, the team licenses the code from Stanford's DARPA Urban Challenge vehicle.
Anthony Lewandowski goes to a local Toyota dealership and buys eight Priuses, takes them back to Google, and retrofits them to accept a computer as a driver. He hooks that computer driver electronically into the brakes, the gas, the steering. These Priuses get a radar system behind the bumper, cameras, a LiDAR system spinning 360 degrees on top.
LiDAR like radar, but it shoots lasers instead of sound waves. At first, the team gives each Prius a cool name, like Knight Rider. But I think we quickly realized that we're not going to be able to name all these vehicles as we scale up our fleet, and so we just started to number them, like, you know, Prius 27. This is Don Burnett. He'd been a researcher working on autonomous submarines.
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Chapter 8: What does the future hold for human drivers in an autonomous world?
They had a real problem. The car was swerving wildly.
It was weaving around like a drunken sailor. And we realized that the scale of the runway was such that you didn't notice the one or two foot kind of oscillation it had in lateral control. And you put it on Central Expressway and suddenly, you know, yep, turns out actually that's a problem.
One more problem to fix. Listening to this story, it's funny because I can imagine it giving me a totally different feeling than it does. A tech company with nobody's permission was testing driverless cars on public roads in California. I don't know why that strikes me as being about invention instead of just hubris and impunity.
Maybe it's because I know that Google would be one of the few tech companies whose driverless cars would not cause any fatal accidents in testing. and that the team would just take more safety precautions than the other companies who'd rush in later to catch up with them once this was an arms race.
The way these cars were designed, the safety driver sat behind the steering wheel, ready to take over. In the other seat was their partner, watching the monitor displaying a graphical interface designed by Dmitry Dolgov. The people watching the screen would call out problems ahead, some discrepancy between what the sensors were seeing and what was actually in the road.
This is what teaching a car to drive actually looked like. Two-person teams manning the cars, logging errors, going back to the office to troubleshoot, and then updating the code. I asked Don Burnett about this era.
And while you're doing this and then like you leave work and you get in your car that you drive as a human, did you find yourself thinking more carefully like, how do I know what I know when I'm driving? Like you're trying to teach a machine by day. Did it affect how you thought about human driving by night? Almost obnoxiously so to any passengers in the car with me.
I was obsessed with one big question, which is why do humans drive the way they drive? And it turns out there were no good answers. And I still think they're not great answers. And instead of actually answering that question, we've just turned to machine learning to infer the deep truths behind why humans do what they do. And so there's some basic principles that you can understand.
Like we try to minimize lateral acceleration, meaning you don't want to be thrown to the outside of your car when you're making a turn. So you're going to slow down. But how much do you slow down, right? And it turns out that's contextual. Don gave me an example. So you're trying to figure out the right speed and angle for the car on one of those tight, curvy on-ramps onto the highway.
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