In the past five years, autonomous driving has gone from “maybe possible” to “definitely possible” to “inevitable” to “how did anyone ever think this wasn’t inevitable?” to “now commercially available.” In December 2018, Waymo, the company that emerged from Google’s self-driving-car project, officially started its commercial self-driving-car service in the suburbs of Phoenix. The details of the program—it’s available only to a few hundred vetted riders, and human safety operators will remain behind the wheel—may be underwhelming but don’t erase its significance. People are now paying for robot rides.
And it’s just a start. Waymo will expand the service’s capability and availability over time. Meanwhile, its onetime monopoly has evaporated. Smaller startups like May Mobility and Drive.ai are running small-scale but revenue-generating shuttle services. Every significant automaker is pursuing the tech, eager to rebrand and rebuild itself as a “mobility provider” before the idea of car ownership goes kaput. Ride-hailing companies like Lyft and Uber are hustling to dismiss the profit-gobbling human drivers who now shuttle their users about. Tech giants like Apple, IBM, and Intel are looking to carve off their slice of the pie. Countless hungry startups have materialized to fill niches in a burgeoning ecosystem, focusing on laser sensors, compressing mapping data, setting up service centers, and more.
This 21st-century gold rush is motivated by the intertwined forces of opportunity and survival instinct. By one account, driverless tech will add $7 trillion to the global economy and save hundreds of thousands of lives in the next few decades. Simultaneously, it could devastate the auto industry and its associated gas stations, drive-thrus, taxi drivers, and truckers. Some people will prosper. Most will benefit. Many will be left behind.
It’s worth remembering that when automobiles first started rumbling down manure-clogged streets, people called them horseless carriages. The moniker made sense: Here were vehicles that did what carriages did, minus the hooves. By the time “car” caught on as a term, the invention had become something entirely new. Over a century, it reshaped how humanity moves and thus how (and where and with whom) humanity lives. This cycle has restarted, and the term “driverless car” will soon seem as anachronistic as “horseless carriage.” We don’t know how cars that don’t need human chauffeurs will mold society, but we can be sure a similar gear shift is on the way.
The First Self-Driving Cars
Just over a decade ago, the idea of being chauffeured around by a string of zeros and ones was ludicrous to pretty much everybody who wasn’t at an abandoned Air Force base outside Los Angeles, watching a dozen driverless cars glide through real traffic. That event was the Urban Challenge, the third and final competition for autonomous vehicles put on by Darpa, the Pentagon’s skunkworks arm.
At the time, America’s military-industrial complex had already thrown vast sums and years of research trying to make unmanned trucks. It had laid a foundation for this technology, but stalled when it came to making a vehicle that could drive at practical speeds, through all the hazards of the real world. So, Darpa figured, maybe someone else—someone outside the DOD’s standard roster of contractors, someone not tied to a list of detailed requirements but striving for a slightly crazy goal—could put it all together. It invited the whole world to build a vehicle that could drive across California’s Mojave Desert, and whoever’s robot did it the fastest would get a million-dollar prize.
Great for spotting things like lane lines on the highway, speed signs, and traffic lights. Some developers think that, with better machine vision, they can use cameras to identify everything they see and navigate accordingly.
The spinning thing you see on top of most self-driving cars is lidar (that’s “light detection and ranging”). It fires out millions of laser beams every second, measures how long they take to bounce back, and uses the data to build a 3D map that’s more precise than what radar offers and easier for a computer to understand than a 2D camera image. It’s also crazy expensive, hard to manufacture at scale, and nowhere near robust enough for a life of potholes and extreme temperatures. Good thing dozens of startups and tech giants are pouring millions of dollars into fixing all that.
At its simplest, this artificial intelligence tool trains computers to do things like detect lane lines and identify cyclists by showing them millions of examples of the subject at hand. Because the world is too complex to write a rule for every possible scenario, it’s key to have cars that can learn from experience and figure out how to navigate on their own.
Before a robocar takes to the streets, its parent company will use cameras and lidars to map its territory in extreme detail. That reference document helps the car verify its sensor readings, and it is key for any vehicle looking to know its own location, down to the centimeter—something standard GPS can’t offer.
A regular presence in cars since the late 1990s, radars bounce radio waves around to see their surrounding and are especially good at spotting big metallic objects—other vehicles. They’re cheap, reliable, and don’t sweat things like fog, rain, or snow.
The 2004 Grand Challenge was something of a mess. Each team grabbed some combination of the sensors and computers available at the time, wrote their own code, and welded their own hardware, looking for the right recipe that would take their vehicle across 142 miles of sand and dirt of the Mojave. The most successful vehicle went just seven miles. Most crashed, flipped, or rolled over within sight of the starting gate. But the race created a community of people—geeks, dreamers, and lots of students not yet jaded by commercial enterprise—who believed the robot drivers people had been craving for nearly forever were possible, and who were suddenly driven to make them real.
They came back for a follow-up race in 2005 and proved that making a car drive itself was indeed possible: Five vehicles finished the course. By the 2007 Urban Challenge, the vehicles were not just avoiding obstacles and sticking to trails but following traffic laws, merging, parking, even making safe, legal U-turns.
When Google launched its self-driving car project in 2009, it started by hiring a team of Darpa Challenge veterans. Within 18 months, they had built a system that could handle some of California’s toughest roads (including the famously winding block of San Francisco’s Lombard Street) with minimal human involvement. A few years later, Elon Musk announced Tesla would build a self-driving system into its cars. And the proliferation of ride-hailing services like Uber and Lyft weakened the link between being in a car and owning that car, helping set the stage for a day when actually driving that car falls away too. In 2015, Uber poached dozens of scientists from Carnegie Mellon University—a robotics and artificial intelligence powerhouse—to get its effort going.