Accession Number:

AD1024611

Title:

Autonomous Intersection Management

Descriptive Note:

[Technical Report, Doctoral Thesis]

Corporate Author:

University of Texas at Austin

Personal Author(s):

• Dresner, Kurt M.

Report Date:

2009-12-01

Pagination or Media Count:

216

Abstract:

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 Current methods of vehicle coordination will be outdated.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 multi agent 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 mechanisms safety, including ways to mitigate some failure modes.

Descriptors:

• computer programs
• artificial intelligence
• autonomous vehicles
• failure mode and effect analysis
• computers
• warning systems
• grids
• reliability
• detectors
• machine learning
• unmanned vehicles
• wireless networks
• aircrafts
• artificial neural networks
• motivation
• probability
• control systems
• generative models
• multiagent systems
• simulators

Subject Categories:

• Cybernetics
• Surface Transportation and Equipment

Distribution Statement:

[A, Approved For Public Release]