The three laws of robotic safety in Isaac Asimov’s science fiction stories seem simple and straightforward, but the ways the fictional tales play out reveal unexpected complexities. Writers of safety standards for self-driving cars express their goals in similarly simple terms. But several groups now developing standards for how autonomous vehicles will interact with humans and with each other face real-world issues much more complex than science fiction.
Advocates of autonomous cars claim that turning the wheel over to robots could slash the horrific toll of 1.3 million people killed around the world each year by motor vehicles. Yet the public has become wary because robotic cars also can kill. Documents released last week by the U.S. National Transportation Safety Board blame the March 2018 death of an Arizona pedestrian struck by a self-driving Uber on safety failures by the car’s safety driver, the company, and the state of Arizona. Even less-deadly safety failures are damning, like the incident where a Tesla in Autopilot mode wasn’t smart enough to avoid crashing into a stopped fire engine whose warning lights were flashing.
Safety standards for autonomous vehicles “are absolutely critical” for public acceptance of the new technology, says Greg McGuire, associate director of the Mcity autonomous vehicle testing lab at the University of Michigan. “Without them, how do we know that [self-driving cars] are safe, and how do we gain public trust?” Earning that trust requires developing standards through an open process that the public can scrutinize, and may even require government regulation, he adds.
Companies developing autonomous technology have taken notice. Earlier this year, representatives from 11 companies including Aptiv, Audi, Baidu, BMW, Daimler, Infineon, Intel, and Volkswagen collaborated to write a wide-ranging whitepaper titled “Safety First for Automated Driving.” They urged designing safety features into the automated driving function, and using heightened cybersecurity to assure the integrity of vital data including the locations, movement, and identification of other objects in the vehicle environment. They also urged validating and verifying the performance of robotic functions in a wide range of operating conditions.
On 7 November, the International Telecommunications Union announced the formation of a focus group called AI for Autonomous and Assisted Driving. It’s aim: to develop performance standards for artificial intelligence (AI) systems that control self-driving cars. (The ITU has come a long way since its 1865 founding as the International Telegraph Union, with a mandate to standardize the operations of telegraph services.)
ITU intends the standards to be “an equivalent of a Turing Test for AI on our roads,” says focus group chairman Bryn Balcombe of the Autonomous Drivers Alliance. A computer passes a Turing Test if it can fool a person into thinking it’s a human. The AI test is vital, he says, to assure that human drivers and the AI behind self-driving cars understand each other and predict each other’s behaviors and risks.
A planning document says AI development should match public expectations so:
• AI never engages in careless, dangerous, or reckless driving behavior
• AI remains aware, willing, and able to avoid collisions at all times
• AI meets or exceeds the performance of a competent, careful human driver
These broad goals for automotive AI algorithms resemble Asimov’s laws, insofar as they bar hurting humans and demand that they obey human commands and protect their own existence. But the ITU document includes a list of 15 “deliverables” including developing specifications for evaluating AIs and drafting technical reports needed for validating AI performance on the road.
A central issue is convincing the public to entrust the privilege of driving—a potentially life-and-death activity—to a technology which has suffered embarrassing failures like the misidentification of minorities that led San Francisco to ban the use of facial recognition by police and city agencies.
Testing how well an AI can drive is vastly complex, says McGuire. Human adaptability makes us fairly good drivers. “We’re not perfect, but we are very good at it, with typically a hundred million miles between fatal traffic crashes,” he says. Racking up that much distance in real-world testing is impractical—and it is but a fraction of the billions of vehicle miles needed for statistical significance. That’s a big reason developers have turned to simulations. Computers can help them run up virtual mileage needed to find potential safety flaws that might arise only rare situations, like in a snowstorm or heavy rain, or on a road under construction.
It’s not enough for an automotive AI to assure the vehicle’s safety, says McGuire. “The vehicle has to work in a way that humans would understand.” Self-driving cars have been rear-ended when they stopped in situations where most humans would not have expected a driver to stop. And a truck can be perfectly safe even when close enough to unnerve a bicyclist.
Other groups are also developing standards for robotic vehicles. ITU is covering both automated driver assistance and fully autonomous vehicles. Underwriters Laboratories is working on a standard for fully-autonomous vehicles. The Automated Vehicle Safety Consortium, a group including auto companies, plus Lyft, Uber, and SAE International (formerly the Society of Automotive Engineers) is developing safety principles for SAE Level 4 and 5 autonomous vehicles. The BSI Group (formerly the British Institute of Standards) developed a strategy for British standards for connected and autonomous vehicles and is now working on the standards themselves.
How long will it take to develop standards? “This is a research process,” says McGuire. “It takes as long as it takes” to establish public trust and social benefit. In the near term, Mcity has teamed with the city of Detroit, the U.S. Department of Transportation, and Verizon to test autonomous vehicles for transporting the elderly on city streets. But he says the field “needs to be a living thing that continues to evolve” over a longer period.
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