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Autonomous Intersection Management: Multi-Intersection Optimization Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone
 

Summary: Autonomous Intersection Management: Multi-Intersection Optimization
Matthew Hausknecht, Tsz-Chiu Au, and Peter Stone
Department of Computer Science
The University of Texas at Austin
Austin, TX 78712
{mhauskn,chiu,pstone}@cs.utexas.edu
Abstract-- Advances in autonomous vehicles and intelligent
transportation systems indicate a rapidly approaching future
in which intelligent vehicles will automatically handle the
process of driving. However, increasing the efficiency of today's
transportation infrastructure will require intelligent traffic
control mechanisms that work hand in hand with intelligent
vehicles. To this end, Dresner and Stone proposed a new
intersection control mechanism called Autonomous Intersection
Management (AIM) and showed in simulation that by studying
the problem from a multiagent perspective, intersection control
can be made more efficient than existing control mechanisms
such as traffic signals and stop signs. We extend their study
beyond the case of an individual intersection and examine the
unique implications and abilities afforded by using AIM-based

  

Source: Au, Tsz-Chiu - Department of Computer Sciences, University of Texas at Austin
Stone, Peter - Department of Computer Sciences, University of Texas at Austin

 

Collections: Computer Technologies and Information Sciences