Door to Door: The Magnificent, Maddening, Mysterious World of Transportation (31 page)

America's parking king, Los Angeles County, is littered with disused lots, which is no surprise in an area where a fantastic 14 percent of the incorporated land area consists of parking. That's the entire sprawling county of 87 suburbs ringing the City of Los Angeles, not just the concentrated urban core (where the percentage of land devoted to parking is closer to 30 percent). LA County boasts 18.6 million parking spaces in all. Nearly half that total consists of lots for business, industry, and government; 5.5 million spaces are taken up by off-street residential parking; and 3.6 million spaces can be found curbside.
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That adds up to 3.3 spaces for every car registered in the county. The space devoted to cars no one is driving covers about 200 square miles, which is 1.4 times the amount of land set aside for LA County freeways and streets. If those spaces were put together into a single parking lot, it would cover an area large enough to hold four San Franciscos.

The age of driverless cars would return all that land to community use. Los Angeles would have four San Franciscos of space to play with. Such an amount of land would be—is—priceless.

The rise of the robots would finally solve the problem of our bankrupt highway trust fund—not through policy brilliance but out of sheer starvation. The national gas tax that already falls so far short of paying drivers' way would wither and die completely in a robot regime if, as many transportation scholars and researchers believe, the ascent of automation drives the rise of electric cars. No gasoline means no gas tax revenue at all, and Congress would be forced to find a new funding source. This would be the perfect opportunity to build user fees right into the emerging autonomous car sharing economy, just as airport funding is quietly built into every airline ticket today. It would be the drive more, pay more model we're supposed to have right now, this time done right. And if politicians need cover for imposing such fees, all they have to say is this shift to automation will end the scourge of car violence, the number one killer of our children. How do you put a price tag on that?

The hardest part will be creating a fair and equitable road tax system during the decades of transition from the old world of human-driven gasoline-powered cars to the new autonomous model. The greatest benefits accrue only after most of the cars on the road are robotic. Most of the costs imposed on the roads—health, safety, and the environment—are caused by the old-school cars we're driving now, which means, in a scrupulously fair world, their owners would pay a greater share of any new transportation tax than driverless electric car users during this transition. Polluters pay, that should be the rule. Safety risks pay. What could be more fair, more market-based than that? This would drive the shift to autonomy more quickly, but it would cause outrage, opposition, and real pain. Doing this part right and fairly would end up being much harder than building the technology itself.

Not everyone is entranced by this driverless car vision, to say the least. David A. Mindell, professor of engineering and the history of technology at the Massachusetts Institute of Technology,
argues that the idea of full autonomy is impractical, even mythic, and that we would be better off striving for a perfect 50-50 blend of human and computer behind the wheel. He uses the 1969 Apollo moon landing as a prime example of the successful merging of human judgment and computer capabilities that led to a safe landing on the surface of the moon. Getting such a blending to work well and safely in everyday driving is a great technical challenge, Mindell argues, but the one most worth pursuing, because it keeps human judgment in the loop for those occasions when that enemy of robotic intelligence—the unexpected—arises.
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This is both a science-based argument and a seductive appeal to the human desire to retain control. But it doesn't jibe with practical experience to date, which has shown that the most hazardous moment for autonomous cars is the hand-off from robot to human control, particularly during some sort of emergency. Startle reflexes, the split-second delay that causes humans to “freeze” before shifting from inattention to concentration to reaction, could make a partially automated system the worst, not the best, of both worlds. “The hardest part is this transition when we have partial automation,” Don Norman, director of the Design Lab at the University of California, San Diego, says. “This has been shown over and over and over again in aviation. People cannot keep their attention on what the task is. Because that is not the way we are built.”

A second problem is Mindell's choice of examples: comparing the primitive Apollo moon-shot digital systems operated by the best trained space pilots in history to today's technology with an ordinary driver at the wheel. That is a bit like comparing that same Apollo command module to the Wright Brothers biplane. It's a mostly useless comparison, and not just because today's computers and software are light-years ahead of Apollo's or because
NASA pilots are trained to pay attention far more rigorously than ordinary car drivers or even because they were concentrating on the mind-blowing moment of
landing on the moon for the first time
versus a somewhat less historic drive to the local Denny's. No, the main problem with the comparison is that it ignores the critical role that detailed 3-D mapping of the driving environment plays in the success of the most advanced driverless car to date, the Google car. The moon landing was a first-time event. The Google car succeeds because it has “learned”—or been programmed—to safely navigate the most well-mapped terrain in the human universe, with over a million test miles under its digital belt, allowing it to adjust to the many variables that are added to that map by the presence of human drivers, cyclists, and pedestrians.

Even imperfect autonomy of this type will be far better than the distracted and imperfect human drivers roaming the highways today, Norman argues. “I am in favor of full automation. It's inevitable and when it comes will save huge numbers of lives, and it will turn out to be a blessing.”

T
he Google car has mastered city driving. Grinding to a halt for a distracted computer-using jaywalker is flashy, but detecting and protecting a wayward pedestrian is actually a fairly easy challenge for the robot. Harder still is detecting a ball bouncing into the street, identifying it as a ball, then realizing that a child might follow that ball into the street and acting accordingly. The Google car can do that, too. But the most impressive achievement, wherein lies the true mastery of city driving, is how it handles the mundane but constantly varying minutiae of navigating through human traffic.

As I'm riding through the streets of Mountain View, just another car in the mix except for the cameras and radars and other
distinctive moon-rover gear bolted to the Lexus test vehicle, the way the car handles a left turn blows me away. It approaches an intersection and signals a turn, then creeps forward without committing to the turn, just as a human does—not to get a better view, because this car sees everything, but to signal the intention to turn to other drivers. Then, the way clear, it executes the turn smoothly and moves on.

Exept for the fact that the driver is always attentive and never lead-footed, the experience is indistinguishable from driving with a human behind the wheel. And the reason for that is, in a way, a human
is
driving. The car doesn't have a supercomputer or advanced artificial intelligence tucked inside. What it does have are lines and lines of code through which human beings have instructed the car what to do at an intersection, when a jaywalker appears, when construction workers block a lane of traffic. That's why Google has driven its fleet of twenty-seven robotic test cars over a million highway and street miles throughout the Bay Area, and why they next moved on to Texas: to experience every possible situation and crisis on streets, so the car can be told by humans how to act. Google has even created what program director Chris Urmson calls his “red team”—a group of mischief makers who try to stump the car with unexpected obstacles or crazy behavior by other cars or cyclists who keep swerving out of their bike lanes. The robot just stops when it doesn't know what to do. But each new line of code eliminates one more stopper. This is how the robot “learns”; it's not about the hardware, which is fairly mature technology. It's all about the software, which is why a Silicon Valley company like Google can compete with Detroit carmakers, most of which have struggled with making software as good as their hardware.

There are still obstacles. Lidar is confused by reflections. The car doesn't know how to handle snow yet. Heavy rain is a problem,
too. The spray of water that kicks up behind other vehicles creates a ghost image on the car's sensors that can appear to be a solid object, something to brake for. Medford says the coders have to figure out how to filter those false positives. There is, he admits, a long to-do list. First among them is the creation of detailed three-dimensional maps that the car uses to navigate. This is the other secret sauce of the Google car: its mapping prowess. These are the
Oxford English Dictionary
of maps, far more detailed than the Google Maps on smartphones and home computers. They measure curb height and sidewalk width, they note bike lanes and median dividers, and they take special note of distinctive markers in the landscape to aid in navigation with far greater precision than GPS coordinates. The maps are not hard to make, Medford says: “You just drive the car in manual mode and the computer builds the map.” So far, the company has mapped all the freeways in the Bay Area and all the streets in the little city of Mountain View, population 77,846. All they have to do is map the rest of the country before bringing the car to market, Medford says. Most people might consider that daunting, but the folks at Google shrug it off as the least interesting challenge before them. “It's just driving,” says Medford. “We'll get to it after we get the technology perfected.”

There have been several accidents involving the Google car—none serious, and none, according to Google, the robot's fault. This is how the Google car's insistence on observing the speed limit and not running red lights can be a safety issue. It's been rear-ended several times, once because it was observing the speed limit on a street full of speeders, and once when it was properly stopped at an intersection when a distracted human plowed into it. Humans in both cars complained of neck pain afterward, but there were no serious injuries. The mix of autonomous and human-driven cars on the road may reduce accidents, but the
greatest gains in safety likely will not occur until the vast majority of vehicles are driverless, Medford says.

The Google car has been pulled over by the police only once: for driving too slowly. It was traveling 25 miles per hour in a 35 mile per hour zone on a four-lane street in Mountain View (which is not technically illegal). The car was not malfunctioning: it is programmed to never exceed 25 miles per hour. The incident went viral, with the little Mountain View police department flooded with inquiries from media around the world. The car got off with a warning.

There's another wrinkle in this transition to vehicles smarter than the humans inside them: the idea of connected cars. This is a technology being pushed by the U.S. Department of Transportation, among others. It's different than driverless systems, but complementary to them. The idea is to leverage wireless technology to connect one vehicle to another—the digerati call this V2V—and to connect cars to the road and street infrastructure—V2I. Connecting cars would be a relatively simple matter, using inexpensive and proven transponders similar to the ones built into every airplane for decades, which transmit position, direction, and speed. These beacons could be anonymous if privacy concerns impede adoption, but the key would be how they could make an autonomous car aware of other vehicles that its sensors might not detect because of range, obstruction, or most relevantly, bad weather. This would be particularly valuable in a transitional mobility universe where autonomous cars and human drivers are mixing it up, as the robot cars would always know in advance if a human driver was slowing down to stop at an intersection—or on a course to just blow through.

The V2I concept would not be so easy to implement, because it would take many years and dollars to wire the built landscape and build beacons into our traffic signals, buildings, parking
structures, and road signs. But such tech would solve the problem of sensor blindness in rain, fog, and snow. The road could literally talk to the cars, and let the autonomous driver know what's being obscured by adverse conditions.

There's no timetable or money at present for getting V2V or V2I up and running, and so all the driverless car projects that are being prepared for market now are designed to operate without any connection to anything—they are intended to be self-sufficient. This has the added bonus of greater digital security, as networked cars, like computers linked to the Internet, would be a far easier target to hack. But these same driverless cars will be able to jack into the connected car and infrastructure systems someday (if such systems ever become available), so security will be a continuing concern.

However it progresses, the transition to driverless cars will not be without some bumps and bruises, if only metaphorically. New technology and change always bring pain with the gains. For all its faults and harm, the hundred-year reign of the traditional car has had a powerful and positive impact on the economy and society, bringing unprecedented mobility and shaping our human landscape, culture, work, and lives for generations. This is part of the miracle of our door-to-door system of systems, and it is almost impossible to imagine replacing it with something else. Cars are linked in mind and deed with Americans' sense of personal freedom and opportunity.

But the current transportation regime is, literally, killing us—with the short-term bang and shatter of crashes, and the longer-term poison of pollution and carbon and oil dependence. Its inefficiencies cost us economically—more than we can pay for now, much less in the future.