This week, a US Department of Transportation report detailed the crashes that advanced driver-assistance systems have been involved in over the past year or so. Tesla’s advanced features, including Autopilot and Full Self-Driving, accounted for 70 percent of the nearly 400 incidents—many more than previously known. But the report may raise more questions about this safety tech than it answers, researchers say, because of blind spots in the data.
The report examined systems that promise to take some of the tedious or dangerous bits out of driving by automatically changing lanes, staying within lane lines, braking before collisions, slowing down before big curves in the road, and, in some cases, operating on highways without driver intervention. The systems include Autopilot, Ford’s BlueCruise, General Motors’ Super Cruise, and Nissan’s ProPilot Assist. While it does show that these systems aren’t perfect, there’s still plenty to learn about how a new breed of safety features actually work on the road.
That’s largely because automakers have wildly different ways of submitting their crash data to the federal government. Some, like Tesla, BMW, and GM, can pull detailed data from their cars wirelessly after a crash has occurred. That allows them to quickly comply with the government’s 24-hour reporting requirement. But others, like Toyota and Honda, don’t have these capabilities. Chris Martin, a spokesperson for American Honda, said in a statement that the carmaker’s reports to the DOT are based on “unverified customer statements” about whether their advanced driver-assistance systems were on when the crash occurred. The carmaker can later pull “black box” data from its vehicles, but only with customer permission or at law enforcement request, and only with specialized wired equipment.
Of the 426 crash reports detailed in the government report’s data, just 60 percent came through cars’ telematics systems. The other 40 percent were through customer reports and claims—sometimes trickled up through diffuse dealership networks—media reports, and law enforcement. As a result, the report doesn’t allow anyone to make “apples-to-apples” comparisons between safety features, says Bryan Reimer, who studies automation and vehicle safety at MIT’s AgeLab.
Even the data the government does collect isn’t placed in full context. The government, for example, doesn’t know how often a car using an advanced assistance feature crashes per miles it drives. The National Highway Traffic Safety Administration, which released the report, warned that some incidents could appear more than once in the data set. And automakers with high market share and good reporting systems in place—especially Tesla—are likely overrepresented in crash reports simply because they have more cars on the road.
It’s important that the NHTSA report doesn’t disincentivize automakers from providing more comprehensive data, says Jennifer Homendy, chair of the federal watchdog National Transportation Safety Board. “The last thing we want is to penalize manufacturers that collect robust safety data,” she said in a statement. “What we do want is data that tells us what safety improvements need to be made.”
Without that transparency, it can be hard for drivers to make sense of, compare, and even use the features that come with their car—and for regulators to keep track of who’s doing what. “As we gather more data, NHTSA will be able to better identify any emerging risks or trends and learn more about how these technologies are performing in the real world,” Steven Cliff, the agency’s administrator, said in a statement.
In the past decade, 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. At first, the program was underwhelming: available only to a few hundred vetted riders, and human safety operators remained behind the wheel. But in the past four years, Waymo has slowly opened the program to members of the public and has begun to run robotaxis without drivers inside. The company has since brought its act to San Francisco. People are now paying for robot rides.
And it’s just a start. Waymo says it will expand the service’s capability and availability over time. Meanwhile, its onetime monopoly has evaporated. Every significant automaker is pursuing the tech, eager to rebrand and rebuild itself as a “mobility provider. Amazon bought a self-driving-vehicle developer, Zoox. Autonomous trucking companies are raking in investor money. 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. Some 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” may 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.
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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.
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.
