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.
The number of American workers who quit their jobs during the pandemic—over a fifth of the workforce—may constitute one of the largest American labor movements in recent history. Workers demanded higher pay and better conditions, spurred by rising inflation and the pandemic realization that employers expected them to risk their lives for low wages, mediocre benefits, and few protections from abusive customers—often while corporate stock prices soared. At the same time, automation has become cheaper and smarter than ever. Robot adoption hit record highs in 2021. This wasn’t a surprise, given prior trends in robotics, but it was likely accelerated by pandemic-related worker shortages and Covid-19 safety requirements. Will robots automate away the jobs of entitled millennials who “don’t want to work,” or could this technology actually improve workers’ jobs and help firms attract more enthusiastic employees?
The answer depends on more than what’s technologically feasible, including what actually happens when a factory installs a new robot or a cashier aisle is replaced by a self-checkout booth—and what future possibilities await displaced workers and their children. So far, we know the gains from automation have proved notoriously unequal. A key component of 20th-century productivity growth came from replacing workers with technology, and economist Carl Benedikt Frey notes that American productivity grew by 400 percent from 1930 to 2000, while average leisure time only increased by 3 percent. (Since 1979, American labor productivity, or dollars created per worker, has increased eight times faster than workers’ hourly compensation.) During this period, technological luxuries became necessities and new types of jobs flourished—while the workers’ unions that used to ensure livable wages dissolved and less-educated workers fell further behind those with high school and college degrees. But the trend has differed across industrialized countries: From 1995 to 2013, America experienced a 1.3 percent gap between productivity growth and median wage growth, but in Germany the gap was only 0.2 percent.
Technology adoption will continue to increase, whether America can equitably distribute the technological benefits or not. So the question becomes, how much control do we actually have over automation? How much of this control is dependent on national or regional policies, and how much power might individual firms and workers have within their own workplaces? Is it inevitable that robots and artificial intelligence will take all of our jobs, and over what time frame? While some scholars believe that our fates are predetermined by the technologies themselves, emerging evidence indicates that we may have considerable influence over how such machines are employed within our factories and offices—if we can only figure out how to wield this power.
While 8 percent of German manufacturing workers left their jobs (voluntarily or involuntarily) between 1993 and 2009, 34 percent of US manufacturing workers left their jobs over the same period. Thanks to workplace bargaining and sectoral wage-setting, German manufacturing workers have better financial incentives to stay at their jobs; The Conference Board reports that the average German manufacturing worker earned $43.18 (plus $8.88 in benefits) per hour in 2016, while the average American manufacturing worker earned $39.03 with only $3.66 in benefits. Overall, Germans across the economy with a “medium-skill” high school or vocational certificate earned $24.31 per hour in 2016, while Americans with comparable education averaged $14.55 per hour. Two case studies illustrate the differences between American and German approaches to manufacturing workers and automation, from policies to supply chains to worker training systems.
In a town on the outskirts of the Black Forest in Baden-Württemberg, Germany, complete with winding cobblestone streets and peaked red rooftops, there’s a 220-person factory that’s spent decades as a global leader in safety-critical fabricated metal equipment for sites such as highway tunnels, airports, and nuclear reactors. It’s a wide, unassuming warehouse next to a few acres of golden mustard flowers. When I visited with my colleagues from the MIT Interactive Robotics Group and the Fraunhofer Institute for Manufacturing Engineering and Automation’s Future Work Lab (part of the diverse German government-supported Fraunhofer network for industrial research and development), the senior factory manager informed us that his workers’ attitudes, like the 14th-century church downtown, hadn’t changed much in his 25-year tenure at the factory. Teenagers still entered the firm as apprentices in metal fabrication through Germany’s dual work-study vocational system, and wages are high enough that most young people expected to stay at the factory and move up the ranks until retirement, earning a respectable living along the way. Smaller German manufacturers can also get government subsidies to help send their workers back to school to learn new skills that often equate to higher wages. This manager had worked closely with a nearby technical university to develop advanced welding certifications, and he was proud to rely on his “welding family” of local firms, technology integrators, welding trade associations, and educational institutions for support with new technology and training.
Our research team also visited a 30-person factory in urban Ohio that makes fabricated metal products for the automotive industry, not far from the empty warehouses and shuttered office buildings of downtown. This factory owner, a grandson of the firm’s founder, complained about losing his unskilled, minimum-wage technicians to any nearby job willing to offer a better salary. “We’re like a training company for big companies,” he said. He had given up on finding workers with the relevant training and resigned himself to finding unskilled workers who could hopefully be trained on the job. Around 65 percent of his firm’s business used to go to one automotive supplier, which outsourced its metal fabrication to China in 2009, forcing the Ohio firm to shrink down to a third of its prior workforce.
While the Baden-Württemberg factory commanded market share by selling specialized final products at premium prices, the Ohio factory made commodity components to sell to intermediaries, who then sold to powerful automotive firms. So the Ohio firm had to compete with low-wage, bulk producers in China, while the highly specialized German firm had few foreign or domestic competitors forcing it to shrink its skilled workforce or lower wages.
Welding robots have replaced some of the workers’ tasks in the two factories, but both are still actively hiring new people. The German firm’s first robot, purchased in 2018, was a new “collaborative” welding arm (with a friendly user interface) designed to be operated by workers with welding expertise, rather than professional robot programmers who don’t know the intricacies of welding. Training welders to operate the robot isn’t a problem in Baden-Württemberg, where everyone who arrives as a new welder has a vocational degree representing at least two years of education and hands-on apprenticeship in welding, metal fabrication, and 3D modeling. Several of the firm’s welders had already learned to operate the robot, assisted by prior trainings. And although the German firm manager was pleased to save labor costs, his main reason for the robot acquisition was to improve workers’ health and safety and minimize boring, repetitive welding sequences—so he could continue to attract skilled young workers who would stick around. Another German factory we visited had recently acquired a robot to tend a machine during the night shift so fewer workers would have to work overtime or come in at night.
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.
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.