Google’s Self-Driving Car Holds Tantalizing Promise, but Major Roadblocks Remain

by Nick Chambers Scientific American

Long a staple of science fiction, self-driving vehicles that act as robot chauffeurs have been a cultural dream for decades. For most of that time, however, the dream seemed a part of some unattainable future.

But now, led in large part by Google’s sudden and unexpected charge, autonomous robot cars come tantalizingly close to reality. As various mapping, sensing and location-based technologies have converged recently, Google has begun to position itself as the leader of our robo-chauffeur future. Yet for all of the technology’s promise, it still has some major—and perhaps insurmountable—hurdles to overcome.

Google estimates that one million lives could be saved around the globe by driverless cars each year. According to the National Highway Traffic Safety Administration (NHTSA), in the U.S. alone there were 5.8 million crashes in 2008. Of those, about 34,000 resulted in fatalities, 1.6 million resulted in injuries and 4.2 million entailed some sort of property damage. The NHTSA says these numbers have come down over time—attesting at least partly to the ever-increasing safety of all vehicles—but they clearly still account for a large amount of deaths, injuries and property damage that driverless cars could drastically reduce.

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Driverless automobiles lack common sense but are getting better at using mapping, GPS and sensing technologies to hold the road.

“Computers are famously devoid of common sense, and you can think of this pre-mapping as a way to bootstrap some common sense into the car,” Urmson says.

Even so, if the world changes between the time the map was assembled and the time the test vehicle drives the route, it can lead to confusion. “There are things that right now are a challenge for us,” Urmson says. “For instance, if most of the world stayed the same but the lanes are shifted—so the physical road didn’t move but, for whatever reason, the department of transportation decided we should drive a half lane to the left—that would probably confuse the car today.”

There are two main components to Google’s efforts: “The first is reliability, which means having the car do the things we expect it to do over and over again; and the second is robustness, which is dealing with unusual situations and still being safe,” Urmson says.

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John Leonard, a Massachusetts Institute of Technology mechanical and ocean engineering professor who led that university’s team to a fourth-place finish in the 2007 DARPA Urban Challenge, thinks that major technological hurdles in robot perception need to be overcome before self-driving cars can be deployed on a large-scale.

“I have tremendous admiration for my colleagues at Google,” Leonard says. “The performance that they have achieved is amazing—for example, their ability to drive at highway speeds. However, because they are building the maps in advance and then having humans pick out stop signs and street lights and crosswalks and so forth, it’s very different than turning a robot loose autonomously in the world with very little prior information.”

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The way M.I.T.’s Leonard sees it, these elements of unreliability are what hinder a place for self-driving cars in our future. “Imagine a situation where a box falls on the road in front of you because it wasn’t strapped down properly,” he says. “The system needs to make a split-second decision to either go straight through it or to swerve left or right—which might have worse consequences than just going forward. The crux of the problem lies in those extreme situations at the tails of the curve that get harder and harder to deal with.”

“Despite all the best efforts of the robot designers, humans still do stupid things,” Leonard says. “Suppose 10 human-generated fatalities are replaced with five robot-generated fatalities, is that an ethical trade that society wants to make?”  Article