Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Artificial Intelligence
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Using adaptive background subtraction into a multi-level model for traffic surveillance
Integrated Computer-Aided Engineering
Identification of anatomic retinal structures for macular delineation in fluorescein angiograms
Integrated Computer-Aided Engineering
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In general, navigation systems estimating a vehicle position is done either by using the Global Positioning System (GPS) or the Dead Reckoning (DR) systems. Other modern estimations are based on the combination of the two systems (GPS/DR). However, the position of a vehicle determined by GPS/DR is far from being perfect since it produces many errors. To solve this problem, a map-matching method is proposed in order to reduce the errors of localization caused by GPS/DR. This algorithm, which uses a digital road map, allows the detection of the correct road where a vehicle moves. In this paper, we introduce a new map-matching algorithm that employs the Transferable Belief Model (TBM). The TBM presents a general justification of belief theory and provides a flexible and adapted representation to manage uncertainty and imprecision. Experimental results show the effectiveness of the utilization of the TBM to the vehicle navigation system.