The Trick That Makes Google's Self-Driving Cars Work

Google is engaging in unprecedented, massive, ongoing data collection to transform intractable problems into solvable chores.
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A snapshot of how a Google car sees the world around it. (Alexis Madrigal)

Google's self-driving cars can tour you around the streets of Mountain View, California. 

I know this. I rode in one this week. I saw the car's human operator take his hands from the wheel and the computer assume control. "Autodriving," said a woman's voice, and just like that, the car was operating autonomously, changing lanes, obeying traffic lights, monitoring cyclists and pedestrians, making lefts. Even the way the car accelerated out of turns felt right. 

It works so well that it is, as The New York Times' John Markoff put it, "boring." The implications, however, are breathtaking. 

Perfect, or near-perfect, robotic drivers could cut traffic accidents, expand the carrying capacity of the nation's road infrastructure, and free up commuters to stare at their phones, presumably using Google's many services. 

But there's a catch. 

Today, you could not take a Google car, set it down in Akron or Orlando or Oakland and expect it to perform as well as it does in Silicon Valley.

Here's why: Google has created a virtual track out of Mountain View. 

A Googler demonstrates what the self-driving car "sees." Note the background world on which the light traffic lights and their sightlines are displayed: that's the track. 

The key to Google's success has been that these cars aren't forced to process an entire scene from scratch. Instead, their teams travel and map each road that the car will travel. And these are not any old maps. They are not even the rich, road-logic-filled maps of consumer-grade Google Maps.

They're probably best thought of as ultra-precise digitizations of the physical world, all the way down to tiny details like the position and height of every single curb. A normal digital map would show a road intersection; these maps would have a precision measured in inches. 

But the "map" goes beyond what any of us know as a map. "Really, [our maps] are any geographic information that we can tell the car in advance to make its job easier," explained Andrew Chatham, the Google self-driving car team's mapping lead.

"We tell it how high the traffic signals are off the ground, the exact position of the curbs, so the car knows where not to drive," he said. "We'd also include information that you can't even see like implied speed limits."

Google has created a virtual world out of the streets their engineers have driven. They pre-load the data for the route into the car's memory before it sets off, so that as it drives, the software knows what to expect. 

"Rather than having to figure out what the world looks like and what it means from scratch every time we turn on the software, we tell it what the world is expected to look like when it is empty," Chatham continued. "And then the job of the software is to figure out how the world is different from that expectation. This makes the problem a lot simpler."

While it might make the in-car problem simpler, but it vastly increases the amount of work required for the task. A whole virtual infrastructure needs to be built on top of the road network!

Very few companies, maybe only Google, could imagine digitizing all the surface streets of the United States as a key part of the solution of self-driving cars. Could any car company imagine that they have that kind of data collection and synthesis as part of their core competency?

Whereas, Chris Urmson, a former Carnegie Mellon professor who runs Google's self-driving car program, oozed confidence when asked about the question of mapping every single street where a Google car might want to operate. "It's one of those things that Google, as a company, has some experience with our Google Maps product and Street View," Urmson said. "We've gone around and we've collected this data so you can have this wonderful experience of visiting places remotely. And it's a very similar kind of capability to the one we use here."

So far, Google has mapped 2,000 miles of road. The US road network has something like 4 million miles of road.

"It is work," Urmson added, shrugging, "but it is not intimidating work." That's the scale at which Google is thinking about this project.

All this makes sense within the broader context of Google's strategy. Google wants to make the physical world legible to robots, just as it had to make the web legible to robots (or spiders, as they were once known) so that they could find what people wanted in the pre-Google Internet of yore. 

In fact, it might be better to stop calling what Google is doing mapping, and come up with a different verb to suggest the radical break they've made with previous ideas of maps. I'd say they're crawling the world, meaning they're making it legible and useful to computers. 

Self-driving cars sit perfectly in-between Project Tango—a new effort to "give mobile devices a human-scale understanding of space and motion"—and Google's recent acquisition spree of robotics companies. Tango is about making the "human-scale" world understandable to robots and the robotics companies are about creating the means for taking action in that world. 

The more you think about it, the more the goddamn Googleyness of the thing stands out: the ambition, the scale, and the type of solution they've come up with to this very hard problem. What was a nearly intractable "machine vision" problem, one that would require close to human-level comprehension of streets, has become a much, much easier machine vision problem thanks to a massive, unprecedented, unthinkable amount of data collection. 

Last fall, Anthony Levandowski, another Googler who works on self-driving cars, went to Nissan for a presentation that immediately devolved into a Q&A with the car company's Silicon Valley team. The Nissan people kept hectoring Levandowski about vehicle-to-vehicle communication, which the company's engineers (and many in the automotive industry) seemed to see as a significant part of the self-driving car solution. 

He parried all of their queries with a speed and confidence just short of condescension. "Can we see more if we can use another vehicle's sensors to see ahead?" Levandowski rephrased one person's question. "We want to make sure that what we need to drive is present in everyone's vehicle and sharing information between them could happen, but it's not a priority."

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Presented by

Alexis C. Madrigal

Alexis Madrigal is the deputy editor of TheAtlantic.com. He's the author of Powering the Dream: The History and Promise of Green Technology. More

The New York Observer has called Madrigal "for all intents and purposes, the perfect modern reporter." He co-founded Longshot magazine, a high-speed media experiment that garnered attention from The New York Times, The Wall Street Journal, and the BBC. While at Wired.com, he built Wired Science into one of the most popular blogs in the world. The site was nominated for best magazine blog by the MPA and best science website in the 2009 Webby Awards. He also co-founded Haiti ReWired, a groundbreaking community dedicated to the discussion of technology, infrastructure, and the future of Haiti.

He's spoken at Stanford, CalTech, Berkeley, SXSW, E3, and the National Renewable Energy Laboratory, and his writing was anthologized in Best Technology Writing 2010 (Yale University Press).

Madrigal is a visiting scholar at the University of California at Berkeley's Office for the History of Science and Technology. Born in Mexico City, he grew up in the exurbs north of Portland, Oregon, and now lives in Oakland.

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