The main tasks of car navigation systems are positioning, routing, and guidance. This paper describes a novel, two-step approach to vehicle positioning founded on the appropriate combination of the in-car sensors, GPS signals, and a digital map. The first step is based on the application of a Kalman filter, which optimally updates the model of car movement based on the in-car odometer and gyroscope measurements, and the GPS signal. The second step further improves the position estimate by dynamically comparing the continuous vehicle trajectory obtained in the first step with the candidate trajectories on a digital map. This is in contrast with standard applications of the digital map where the current position estimate is simply projected on the digital map at every sampling instant.

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Car navigation systems have three main tasks: positioning, routing and navigation (guidance). Positioning of the car is carried out hy appropriately combining information from several senson and information sources including odometers, gyroscopes, the GPS information and the digital map. This paper describes two-sensor fusion steps implemented in the commercial Siemens car navigation systems. The first step is the fusion of the odometer, gyroscope, and GPS sensory information. The dynamic model of the car movement is implemented in a Kalman Filter, which relays on the GPS signal as a teacher. In the second step the available digital map is used to find the most likely position on the roads. Contrary to the standard application of the digital map where the current estimated car position is just projected on the road map, the herein presented approach compares the features of the integrated vehicle path with the features of the candidate roads from the digital map. In addition, this paper presents the results of the experimental drives. The developed car navigation system was awarded in 2002 by Auto Build magazine as the hest car navigation systems among ten competing systems.

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