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Mining GPS Data to Augment Road Models

Seth Rogers Pat Langley Christopher Wilson
DaimlerChrysler Research and Technology Center
1510 Page Mill Road, Palo Alto, CA 94304 USA
(rogers, langley, wilson)


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 paper 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.

Keywords: Background knowledge, noisy data, incremental algorithms, implementation and use of KDD systems, case studies, evaluating knowledge and potential discoveries.

Printable Postscript version (214K, 11 pages)

next up previous
Next: Introduction
Seth Rogers