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Digital mapping

High-accuracy geospatial databases are needed for many ITS applications. The IV Lab has developed techniques to rapidly create centimeter-accurate digital maps, which can be incorporated into intelligent vehicle guidance systems. In addition to mapping lane boundaries and in-roadway structures, these databases include obstacles near the roadway such as signs and trees; mapping these obstacles makes it possible to remove them from the virtual scene presented in a head-up display.

Although the concept of a “digital map” is appealing as a way to describe the geospatial databases, existing digital maps fall far short of the requirements of many ITS applications. For example:

  • The standard TIGER (Topographically Integrated Geographic Encoding and Referencing) maps compiled by the Census Bureau are based on 1:100,000 resolution—giving a functional accuracy of 50–60 meters.
  • The familiar 1:24,000 scale topographical maps created by the United States Geological Survey, stored as digital raster graphics (DRGs), are accurate to roughly 15 meters.
  • USGS Digital Ortho Quad (DOQ) maps, based on digitized aerial photographs, use a 1:12,000 scale, equivalent to three-meter accuracy horizontally.

In contrast, geospatial databases developed in the IV Lab identify and locate all relevant fixed landscape elements local to the road, including land boundaries, guardrails, dividers, bridge abutments, and signs, as well as attributes like intersections, speed regulations, etc. The accuracy of these databases is 20cm or better.

Further, this geospatial database is designed for real-time access by a moving vehicle, which requires minimal latency. Standard geographic information system (GIS) tools are simply not fast enough to provide real time information to an onboard computer system. The structure of the database and the query engine were designed especially for this application.

To populate the database, the research team employed a number of tools and techniques, including data from the Minnesota Department of Transportation (Mn/DOT) photogrammetric unit, surveying systems from the Mn/DOT Metro GIS unit, and vehicle ‘drive-overs’.

Objects that exist within the database include:

  • LaneBoundary—the leftmost and rightmost limits of each individual lane (green)
  • RoadShoulder—the extent of any driveable surface (red)
  • RoadIsland—areas within RoadShoulder objects which are not drivable (blue)
  • LaneCenter—Midpoints between lane boundaries (black)


Driver-assistive vision enhancement systems developed in the IV Lab uses data from an onboard geospatial database to display lane lines and other road features to the driver in real time via a head-up display.

A geospatial database is used in the IV Lab’s Rural Intersection Collision Avoidance research to improve the ability of the radar system to determine whether a target represents a legitimate threat at the intersection—distinguishing between a vehicle approaching the intersection and a non-vehicle object within the sensors’ field of view.