Nav: Home

Advancing self-driving car design, other shared human- and machine-controlled systems

July 12, 2016

AMHERST, Mass. - University of Massachusetts Amherst computer science graduate students Kyle Wray and Luis Pineda, with their professor Shlomo Zilberstein, today described a new approach to managing the challenge of transferring control between a human and an autonomous system, in a paper they presented at the International Joint Conference on Artificial Intelligence in New York City.

Their theoretical work, tested in experiments in a driving simulator, should help to advance the development of safe semi-autonomous systems (SAS) such as self-driving cars. Such systems rely on human supervision and occasional transfer of control between the human and the automated systems, Zilberstein explains. With substantial support from the National Science Foundation and the auto industry, his lab is working on new approaches to SAS that are controlled collaboratively by a person and a machine while each capitalizes on their distinct abilities.

"Self-driving cars are coming," says Zilberstein, "but the world is fairly chaotic and not many autonomous systems can cope with that yet. My sense is that we're pretty far from having fully autonomous systems in cars." This is because artificial intelligence sensing and decision-making techniques are still limited and no matter how much training and design are used, there is no sufficiently accurate model of the real world that allows such systems to operate reliably.

For example, he suggests, "Trains might be next as a candidate for autonomy, but even then, with a downed branch on the track during a storm, a person may be needed to judge how to proceed safely."

The researcher says the example highlights a significant challenge that SAS research must address, that is, transferring control quickly, safely and smoothly between the system and the person supervising it. Most systems designed to date do not accomplish this. "Paradoxically," says Zilberstein, "as we introduce more autonomy, people become less engaged with the operation of the system and it becomes harder for them to take over control." In the paper presented today, to be published in the conference proceedings, the researchers establish precise requirements to assure that controlling entities can act reliably.

They apply the theoretical framework to semi-autonomous vehicles using a hierarchical or step-wise approach with two levels of reasoning. The high-level route planning takes into account the occasional need to transfer control, without planning it in detail. The actual transfer of control is managed by a detailed, "high-fidelity" model that notifies drivers of their expected actions and constantly monitors their reactions. It can handle situations by stopping the vehicle, for example, when the driver does not respond to the request to take over control, Zilberstein explains. Their analysis of the integrated model shows that it provides important safety guarantees.

The researchers show how to apply this general framework to SAS for vehicles and demonstrate that it maintains what they call "live state." Intuitively, this yields what they call "strong semi-autonomy," meaning that the system is never placed under the responsibility of an entity that is not prepared to handle the situation. Their experiments show that this approach uses both human and vehicle strengths well.

Zilberstein and colleagues plan to integrate this approach using a large-scale realistic driving simulator in collaboration with professors Donald Fisher and Siby Samuel, as well as postdoctoral fellow Timothy Wright of the Arbella Human Performance Lab in UMass Amherst's College of Engineering.

Developing reliable ways to transfer control back to the driver when an anomaly is detected is a crucial component of deploying self-driving cars. This work will allow the researchers to validate the new approach with human drivers controlling a self-driving car while performing a variety of tasks.
-end-


University of Massachusetts at Amherst

Related Artificial Intelligence Articles:

Artificial intelligence improves biomedical imaging
ETH researchers use artificial intelligence to improve quality of images recorded by a relatively new biomedical imaging method.
Evolution of learning is key to better artificial intelligence
Researchers at Michigan State University say that true, human-level intelligence remains a long way off, but their new paper published in The American Naturalist explores how computers could begin to evolve learning in the same way as natural organisms did -- with implications for many fields, including artificial intelligence.
Artificial intelligence probes dark matter in the universe
A team of physicists and computer scientists at ETH Zurich has developed a new approach to the problem of dark matter and dark energy in the universe.
Artificial intelligence used to recognize primate faces in the wild
Scientists at the University of Oxford have developed new artificial intelligence software to recognize and track the faces of individual chimpanzees in the wild.
The brain inspires a new type of artificial intelligence
Using advanced experiments on neuronal cultures and large scale simulations, scientists at Bar-Ilan University have demonstrated a new type of ultrafast artifical intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms.
More Artificial Intelligence News and Artificial Intelligence Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Rethinking Anger
Anger is universal and complex: it can be quiet, festering, justified, vengeful, and destructive. This hour, TED speakers explore the many sides of anger, why we need it, and who's allowed to feel it. Guests include psychologists Ryan Martin and Russell Kolts, writer Soraya Chemaly, former talk radio host Lisa Fritsch, and business professor Dan Moshavi.
Now Playing: Science for the People

#537 Science Journalism, Hold the Hype
Everyone's seen a piece of science getting over-exaggerated in the media. Most people would be quick to blame journalists and big media for getting in wrong. In many cases, you'd be right. But there's other sources of hype in science journalism. and one of them can be found in the humble, and little-known press release. We're talking with Chris Chambers about doing science about science journalism, and where the hype creeps in. Related links: The association between exaggeration in health related science news and academic press releases: retrospective observational study Claims of causality in health news: a randomised trial This...