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 b...

Current digital maps are inadequate for many advanced information applications in vehicles. One shortcoming is the lack of lane geometry models with sufficient precision. This paper discuss several applications of a lane model database and presents an inexpensive method of inducing lane models by analyzing unsupervised traces of vehicle positions 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. We also use the lane models to predict when a vehicle is changing lanes and compare this with ground truth. In addition to lane models, the analysis of position traces provides a cheap, automated method of deriving other road fea...

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 system that refines and augments commercial digital maps 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. We also present a number of evaluation metrics for estimating the quality of the enhanced maps we generate. 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 Sensors (vehicle-based), Communications (vehicle-to-roadside),...