Book Description

ABSTRACT: The rate of development of autonomous vehicles over the last five years has been remarkable. Advancements have been made at such a pace that between DARPA's first Grand Challenge in 2004 and the Urban Challenge in 2007, vehicles have gone from failing to complete basic navigation tasks to successfully navigating urban streets. Significant progress has been made in the controls and planning areas, with navigation abilities improving as a partial result of improvements in Global Positioning precision. Hardware advancements have been made as well, with both computers and sensors increasing data storage and processing capabilities. As a result, recent research has focused on combining the advanced computational abilities of computers with the increasingly relevant data provided by local sensors. This thesis deals with combining advancements in the fields of sensing and controls to improve upon existing autonomous navigation architectures. The Lane Finder Arbiter, a software component created for this research, provides an interface between raw sensing components and vehicle navigation components. The problem statement is first described, followed by a review of similar research and a description of prior research within the Center of Intelligent Machines and Robotics (CIMAR) that led to the creation of the Lane Finder Arbiter. The Lane Finder Arbiter, the focus of the thesis, is then described. The statistical methods used for this research are then discussed, and finally the results obtained from testing are analyzed. These results are used to draw conclusions about the Lane Finder Arbiter's current strengths, as well as possible future improvements to the new navigation architecture.