Artificial intelligence research is ushering in an era of sophisticated,
mass-market transportation technology. While computers can fly a
passenger jet better than a human pilot, people still face the dangerous
yet tedious task of driving. Intelligent Transportation Systems (ITS) is
the field focused on integrating information technology with vehicles
and transportation infrastructure. Recent advances in ITS point to a
future in which vehicles handle the vast majority of the driving
task. Once autonomous vehicles become popular, interactions amongst
multiple vehicles will be possible. Current methods of vehicle
coordination will be outdated. The bottleneck for efficiency will no
longer be drivers, but the mechanism by which those drivers' actions are
coordinated.
Current methods for controlling traffic cannot exploit the superior
capabilities of autonomous vehicles. This thesis describes a novel approach
to managing autonomous vehicles at intersections that decreases the
amount of time vehicles spend waiting. Drivers and intersections in this
mechanism are treated as autonomous agents in a multiagent system. In
this system, agents use a new approach built around a detailed
communication protocol, which is also a contribution of the thesis. In
simulation, I demonstrate that this mechanism can significantly
outperform current intersection control technology-traffic signals and
stop signs.
This thesis makes several contributions beyond the mechanism and
protocol. First, it contains a distributed, peer-to-peer version of the
protocol for low-traffic intersections. Without any requirement of
specialized infrastructure at the intersection, such a system would be
inexpensive and easy to deploy at intersections which do not currently
require a traffic signal. Second, it presents an analysis of the
mechanism's safety, including ways to mitigate some failure
modes. Third, it describes a custom simulator, written for this work,
which will be made publicly available following the publication of the
thesis. Fourth, it explains how the mechanism is "backward-compatible"
so that human drivers can use it alongside autonomous vehicles. Fifth,
it explores the implications of using the mechanism at multiple proximal
intersections. The mechanism, along with all available
modes of operation, is implemented and tested in simulation, and I
present experimental results that strongly attest to the efficacy of
this approach. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-12-689 |
| Date | 24 August 2010 |
| Creators | Dresner, Kurt Mauro |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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