Mining GPS Data to Augment Road Models
Seth Rogers
DaimlerChrysler Research and Technology
Palo Alto, California
rogers@RTNA.DaimlerChrysler.COM
Many advanced safety and navigation applications in vehicles require accurate, detailed digital maps, but manual lane measurements are expensive and time-consuming, making automated techniques desirable. This talk describes a data-mining approach to map refinement, using position traces that come from Global Positioning System receivers with differential corrections. The computed lane models enable safety applications, such as lanekeeping, and convenience applications, such as lane-changing advice. Experiments show that, starting from a baseline map that is commercially available, our lane models predict a vehicle's lane with high accuracy from a small number of passes over a particular road segment. Multiple position traces are a powerful new source of data that enables cheap, automated methods of inducing lane models, as well as other geographic knowledge, like traffic signals and elevations, and potentially impacts any geographic information system with a need to relate to actual behavior.
This talks describes joint work with Chris Wilson and Philip Tsai.
Date: Thurs., Oct. 7, 1999 |
Time: 4:15-5:30PM |
Place: Cordura 100 |
Return to the seminar schedule