Mining GPS data to augment road models
Seth Rogers, Pat Langley, Christopher K. Wilson · 1999
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.'The GPS receivers used in this study are generally accurate to between 1 and 2 meters, whereas road lanes are about 3 to 4 meters wide.Pemissjon to make digital or hard copies of all or part of this work fol personal or classroom use is granted without fee provided that cwics are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.To CWY otherwise, to republish, to post on seners or to redistrihutc 10 Ms. requires prior specific permission and/or a fee.