Global Positioning System Reference
In-Depth Information
location on a roadway and a given direction of travel. Conditional tests are applied to
determine whether the vehicle is traveling on the known road by comparing turns from the
vehicle location to a segment of the digital road map. A correction is performed whenever
the heading of the vehicle changes (Morisue & Ikeda, 1989). However, for this technique to
work, the vehicle is generally assumed to follow a predetermined road. There is
considerable uncertainty when the vehicle travels off-road because there is no longer any
way to correct for errors (Zhao, 1997; Czerniak, 2002).
2.2 Probabilistic map-matching
The probabilistic approach, described later, has the advantage of not assuming that the
vehicle is always on a road. Vehicle heading error is calculated with an elliptical or
rectangular confidence region and error models are developed within which the true vehicle
location can be determined. If the vehicle position within the region contains one
intersection or road segment, a match is made and the coordinates on the road are used in
the next position calculation. If more than one road or intersection lies within the region,
connectivity checks are made to determine the most probable location of the vehicle given
earlier vehicle positions. As a result, the algorithm yields the best match segment along with
the most probable matching point on the segment (Zhao, 1997; Czerniak, 2002).
2.3 Fuzzy logic map-matching
Fuzzy logic is an effective way to deal with tasks that involve qualitative terms and
concepts, vagueness, and human intervention. Expert knowledge and experiences employed
by a fuzzy logic based map-matching algorithm are represented as a set of rules to
determine vehicle location (e.g., if the difference between the orientation of the roadway
segment and the heading of the vehicle is small, then resemblance between the vehicle travel
path and the candidate route is high).
S. Kim and J. H. Kim (2001) propose an adaptive fuzzy-network-based C-measure algorithm
that identifies the roadway on which a vehicle is traveling by comparing C-measures
associated with each candidate roadway. These measures are membership functions that
represent the certainty of the existence of a vehicle on a specific roadway. After the roadway
is identified, the algorithm determines the vehicle position on the roadway by orthogonal
projection. The algorithm requires the distance between the vehicle's GPS coordinates and
its projected position on the roadway to be small. Furthermore, the shape of the roadway
must be similar to the trajectory of the vehicle.
Jagadeesh et al. (2004) developed a map-matching algorithm based on the inferences and a
simple fuzzy rule set. This algorithm evaluates the likelihood of candidate roads to be the
actual traveled road. Three fuzzy rules are employed for this purpose, which include
heading comparison, road resemblance, and verification of off road vehicles. Test results
with simulated data indicate that the algorithm is capable of achieving high accuracy.
Quddus et al. (2006) describe a map-matching algorithm based on fuzzy logic theory. The
proposed algorithm employs an integrated navigation system and digital map data to
identify the correct link and determine the vehicle location on the selected link. Although
the algorithm was tested successfully in different road networks, the authors consider that
future evaluation of the algorithm is required under urban conditions.
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