UMass researchers improve artificial intelligence algorithms
for semi-autonomous vehicles.
For decades, researchers in artificial intelligence, or AI, worked
on specialized problems, developing theoretical concepts and
workable algorithms for various aspects of the field. Computer
vision, planning and reasoning experts all struggled
independently in areas that many thought would be easy to
solve, but which proved incredibly difficult.
However, in recent years, as the individual aspects of artificial
intelligence matured, researchers began bringing the pieces
together, leading to amazing displays of high-level
intelligence: from to the
to the ability of AI to .
These advances were on display this week at the 29th
conference of the
(AAAI) in Austin, Texas, where interdisciplinary
and applied research were prevalent, according to Shlomo
Zilberstein, the conference committee chair and co-author on
three papers at the conference.
Zilberstein studies the way artificial agents plan their future
actions, particularly when working semi-autonomously--that
is to say in conjunction with people or other devices.
Examples of semi-autonomous systems include co-robots
working with humans in manufacturing, search-and-rescue
robots that can be managed by humans working remotely
and "driverless" cars. It is the latter topic that has particularly
piqued Zilberstein's interest in recent years.
The marketing campaigns of leading auto manufacturers have
presented a vision of the future where the passenger (formerly
known as the driver) can check his or her email, chat with
friends or even sleep while shuttling between home and the
office. Some prototype vehicles included seats that swivel
back to create an interior living room, or as in the case of
Google's driverless car, a design with no steering wheel or
brakes.
Except in rare cases, it's not clear to Zilberstein that this
vision for the vehicles of the near future is a realistic one.
"In many areas, there are lots of barriers to full autonomy,"
Zilberstein said. "These barriers are not only technological,
but also relate to legal and ethical issues and economic
concerns."
In his talk at the "Blue Sky" session at AAAI, Zilberstein
argued that in many areas, including driving, we will go
through a long period where humans act as co-pilots or
supervisors, passing off responsibility to the vehicle when
possible and taking the wheel when the driving gets tricky,
before the technology reaches full autonomy (if it ever does).
In such a scenario, the car would need to communicate with
drivers to alert them when they need to take over control. In
cases where the driver is non-responsive, the car must be
able to autonomously make the decision to safely move to the
side of the road and stop.
"People are unpredictable. What happens if the person is not
doing what they're asked or expected to do, and the car is
moving at sixty miles per hour?" Zilberstein asked. "This
requires 'fault-tolerant planning.' It's the kind of planning that
can handle a certain number of deviations or errors by the
person who is asked to execute the plan."
With support from the National Science Foundation (NSF),
Zilberstein has been exploring these and other practical
questions related to the possibility of artificial agents that act
among us.
Zilberstein, a professor of computer science at the University
of Massachusetts Amherst, works with human studies experts
from academia and industry to help uncover the subtle
elements of human behavior that one would need to take into
account when preparing a robot to work semi-autonomously.
He then translates those ideas into computer programs that
let a robot or autonomous vehicle plan its actions--and create
a plan B in case of an emergency.
There are a lot of subtle cues that go into safe driving. Take
for example a four-way stop. Officially, the first car to the
crosswalk goes first, but in actuality, people watch each other
to see if and when to make their move.
"There is a slight negotiation going on without talking,"
Zilberstein explained. "It's communicating by your action such
as eye contact, the wave of a hand, or the slight revving of an
engine."
In trials, autonomous vehicles often sit paralyzed at such
stops, unable to safely read the cues of the other drivers on
the road. This "undecidedness" is a big problem for robots. A
recent paper by Alan Winfield of Bristol Robotics Laboratory in
the UK showed how robots, when faced with a difficult
decision, will often process for such a long period of time as
to miss the opportunity to act. Zilberstein's systems are
designed to remedy this problem.
"With some careful separation of objectives, planning
algorithms could address one of the key problems of
maintaining 'live state', even when goal reachability relies on
timely human interventions," he concluded.
The ability to tailor one's trip based on human-centered
factors--like how attentive the driver can be or the driver's
desire to avoid highways--is another aspect of semi-
autonomous driving that Zilberstein is exploring.
In a paper with Kyle Wray from the University of
Massachusetts Amherst and Abdel-Illah Mouaddib from the
University of Caen in France, Zilberstein introduced a new
model and planning algorithm that allows semi-autonomous
systems to make sequential decisions in situations that
involve multiple objectives--for example, balancing safety and
speed.
Their experiment focused on a semi-autonomous driving
scenario where the decision to transfer control depended on
the driver's level of fatigue. They showed that using their new
algorithm a vehicle was able to favor roads where the vehicle
can drive autonomously when the driver is fatigued, thus
maximizing driver safety.
"In real life, people often try to optimize several competing
objectives," Zilberstein said. "This planning algorithm can do
that very quickly when the objectives are prioritized. For
example, the highest priority may be to minimize driving time
and a lower priority objective may be to minimize driving
effort. Ultimately, we want to learn how to balance such
competing objectives for each driver based on observed
driving patterns."
It's an exciting time for artificial intelligence. The fruits of
many decades of labor are finally being deployed in real
systems and machine learning is being adopted widely and for
different purposes than anyone had ever realized.
"We are beginning to see these kinds of remarkable successes
that integrate decades-long research efforts in a variety of AI
topics," said Héctor Muñoz-Avila, program director in NSF's
Robust Intelligence cluster.
Indeed, over many decades, NSF's Robust Intelligence
program has supported foundational research in artificial
intelligence that, according to Zilberstein, has given rise to the
amazing smart systems that are beginning to transform our
world. But the agency has also supported researchers like
Zilberstein who ask tough questions about emerging
technologies.
"When we talk about autonomy, there are legal issues,
technological issues and a lot of open questions," he said.
"Personally, I think that NSF has been able to identify these
as important questions and has been willing to put money
into them. And this gives the U.S. a big advantage."
Investigators
Shlomo Zilberstein
Donald L. Fisher
Claudia Goldman
Kyle Hollins Wray
Luis Pineda
Avinoam Borowsky
Richard G. Freedman
Abdel-Illah Mouaddib
Related Programs
Robust intelligence
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