Mining GPS data to augment road models

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.

[1]  Dorota A. Grejner-Brzezinska,et al.  Positioning Accuracy of the GPSVan , 1996 .

[2]  Seth Rogers,et al.  THE POTENTIAL OF PRECISION MAPS IN INTELLIGENT VEHICLES , 1998 .

[3]  U. Franke,et al.  COMPUTER VISION ON THE ROAD : A LANE DEPARTURE AND DROWSY DRIVER WARNING SYSTEM , 1995 .

[4]  Christopher Kenneth Hoover Wilson,et al.  An Integrated Vehicle Positioning System for Safety Applications , 2000 .

[5]  Yolanda Gil,et al.  Acquiring domain knowledge for planning by experimentation , 1992 .

[6]  Mei Chen,et al.  AURORA: a vision-based roadway departure warning system , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[7]  Raymond J. Mooney,et al.  Theory Refinement Combining Analytical and Empirical Methods , 1994, Artif. Intell..

[8]  Dariu Gavrila,et al.  The Issues , 2011 .

[9]  Douglas J. Pearson.  Learning Procedural Planning Knowledge in Complex Environments , 1996, AAAI/IAAI, Vol. 2.

[10]  Seth Rogers,et al.  Learning To Associate Observed Driver Behavior with Traffic Controls , 1999 .

[11]  Dean A. Pomerleau,et al.  RALPH: rapidly adapting lateral position handler , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[12]  Pat Langley,et al.  Learning Cooperative Lane Selection Strategies for Highways , 1998, AAAI/IAAI.

[13]  Xuemei Wang,et al.  Learning Planning Operators by Observation and Practice , 1994, AIPS.

[14]  S. Teller.  Automated urban model acquisition : Project rationale and status , 1999 .

[15]  Pat Langley,et al.  Learning to Predict the Duration of an Automobile Trip , 1998, KDD.

[16]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .