Artificial intelligence research is ushering in an era of sophisticated, mass-markettransportation technology. While computers can fly a passenger jet better than ahuman pilot, people still face the dangerous yet tedious task of driving. IntelligentTransportation Systems (ITS) is the field focused on integrating information technology with vehicles and transportation infrastructure. Recent advances in ITSpoint to a future in which vehicles handle the vast majority of the driving task. Once autonomous vehicles become popular, interactions amongst multiple vehicles will bepossible. Current methods of vehicle coordination will be outdated. The bottleneck for efficiency will no longer be drivers, but the mechanism by which those driversactions are coordinated.Current methods for controlling traffic cannot exploit the superior capabilities of autonomous vehicles. This thesis describes a novel approach to managing autonomousvehicles at intersections that decreases the amount of time vehicles spendwaiting. Drivers and intersections in this mechanism are treated as autonomousagents in a multiagent system. In this system, agents use a new approach builtaround a detailed communication protocol, which is also a contribution of the thesis.In simulation, I demonstrate that this mechanism can significantly outperformcurrent intersection control technologytraffic 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-trafficintersections. Without any requirement of specialized infrastructure at the intersection,such a system would be inexpensive and easy to deploy at intersections whichdo not currently require a traffic signal. Second, it presents an analysis of the mechanismssafety, including ways to mitigate some failure modes.
Autonomous Intersection Management
2009
216 pages
Report
Keine Angabe
Englisch
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