Multimodal Fusion for Car Navigation Systems

  • Dragan Obradovic
  • Henning Lenz
  • Markus Schupfner
  • Kai Heesche

The main tasks of car navigation systems are positioning, routing, and guidance. This chapter 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. In addition to the positioning problem, this chapter addresses a fuzzy-logic based approach to guidance.


Global Position System State Machine Navigation System Dead Reckoning Global Position System Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Dragan Obradovic
    • 1
  • Henning Lenz
    • 1
  • Markus Schupfner
    • 2
  • Kai Heesche
    • 1
  1. 1.Siemens AGMunichGermany
  2. 2.Becker Automotive Systems GmbHMunichGermany

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