Planning for Improving Throughput in Autonomous Intersection Management

Speaker:	Dr. Tsz-Chiu AU
		Department of Computer Science
		University of Texas at Austin

Title:		"Planning for Improving Throughput in Autonomous
		 Intersection Management"

Date:		Wednesday, 17 Novemebr 2010

Time:		4:00pm - 5:00pm

Venue:		Room 3315 (via lifts 17/18), HKUST

Abstract:

Fully autonomous vehicles are technologically feasible with the current
generation of hardware, as demonstrated by recent robot car competitions
such as 2007 DARPA Urban Challenge.  This milestone creates an opportunity
to reconsider modern transportation infrastructure, investigating more
efficient systems that leverage a mostly autonomous vehicle population.
Dresner and Stone proposed a new intersection control mechanism called
Autonomous Intersection Management (AIM) and showed that intersection
control can be made more efficient than traditional control mechanisms,
including traffic signals and stop signs. In this talk, I will present a
study of the relationship between the precision of cars' motion
controllers and the efficiency of the intersection controller. I will then
describe a planning-based motion controller that can increase the
efficiency of the autonomous intersection control mechanism by reducing
the chance that autonomous vehicles stop before intersections.  Finally, I
will present some more recent developments of this project, focusing
especially on a mixed reality experiment platform on which a physical
vehicle can interact with many simulated vehicles.


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Biography:

Tsz-Chiu Au is a postdoctoral fellow in the Department of Computer Science
at the University of Texas at Austin.  He graduated with a Ph.D. degree in
Computer Science at the University of Maryland, College Park in 2008. His
research interests are multi-agent systems, intelligent transportation
systems, AI planning, and case-based reasoning.