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Intersections of the Future: Using Fully Autonomous Vehicles

Title:  Intersections of the Future: Using Fully Autonomous Vehicles


Date/Time:  Wednesday, August 15th at 6:30 PM


Artificial intelligence research is ushering in a new era of
sophisticated, mass-market transportation technology. While computers
can already fly a passenger jet better than a trained human pilot,
people are still faced with the dangerous yet tedious task of driving
automobiles. Intelligent Transportation Systems (ITS) is the field of
study that aims to use artificial intelligence to make transportation
safer, cheaper, and more efficient. Recent advances in ITS point to a
future in which vehicles themselves handle the vast majority of the
driving task. Once autonomous vehicles become popular, autonomous
interactions amongst *multiple* vehicles will be possible. Current
methods of vehicle coordination, which are all designed to work with
human drivers, will be outdated. The bottleneck for roadway efficiency
will no longer be the drivers, but rather the mechanism by which those
drivers' actions are coordinated. While open-road driving is a
well-studied and more-or-less-solved problem, urban traffic scenarios,
especially intersections, are much more challenging.

This talk will address the question: "To what extent and how can a
multiagent intersection control mechanism take advantage of the
capabilities of autonomous vehicles in order to make automobile travel
safer and faster?'' First, I will introduce and specify the problem of
intersection management as a multiagent system and define a metric by
which solutions can be evaluated. Next, I will propose a novel
multiagent intersection control mechanism in which autonomous driver
agents "call ahead'' and reserve space-time in the intersection,
pending the approval of an arbiter agent called an intersection manager,
which is located at the intersection.



Dr. Peter Stone is an Alfred P. Sloan Research Fellow, Guggenheim
Fellow, AAAI Fellow, Fulbright Scholar, and Professor in the Department
of Computer Sciences at the University of Texas at Austin. He received
his Ph.D in Computer Science in 1998 from Carnegie Mellon University.
From 1999 to 2002 he was a Senior Technical Staff Member in the
Artificial Intelligence Principles Research Department at AT&T Labs -
Research. Peter's research interests include machine learning,
multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER
award from the National Science Foundation for his research on learning
agents in dynamic, collaborative, and adversarial multiagent
environments. In 2004, he was named an ONR Young Investigator for his
research on machine learning on physical robots. In 2007, he was awarded
the prestigious IJCAI 2007 Computers and Thought award, given once every
two years to the top AI researcher under the age of 35.



Room Number: Suite 100
12357-C Riata Trace Parkway
Bldg 7, Suite 100
Austin,  Texas
United States 78727

Meeting Agenda:

6:30 p.m. Networking and Gathering
7:00 p.m. Call to Order, Announcement
7:15 p.m. Presentation, with Q/A
8:45 p.m. Meeting Evaluation, Adjourn