Multi-sensor Fusion Method Using Bayesian Network for Precise Multi-vehicle Localization

Cherif Smaili 1 François Charpillet 1 Maan El Badaoui El Najjar 1, 2
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 STF - Systèmes Tolérants aux Fautes
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : The multi-sensor fusion approach for multi-vehicle localization presented in this paper is based on the use of Bayesian network in order to fuse measurements sensors. For each vehicle, a Bayesian network is implemented to fuse measurement of embedded sensors. For the train of vehicle localization, a global Bayesian network is implemented in which we have modelled vehicles interconnections. The leader vehicle is supposed to be equipped by especially accurate sensors. With this approach, one can see that the follower's geo-positions computing are quite improved in using the Leader vehicle path and followers relative positioning provide for each follower using a rangefinder. Real data sensors are used to validate and to test the proposed approach. Experimental results are presented to shown approach performance.
Type de document :
Communication dans un congrès
The 11th International IEEE Conference on Intelligent Transportation Systems, Oct 2008, Beijing,, China. IEEE, 2008
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https://hal.inria.fr/inria-00339316
Contributeur : Cherif Smaili <>
Soumis le : lundi 17 novembre 2008 - 15:19:27
Dernière modification le : mardi 3 juillet 2018 - 11:27:34

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  • HAL Id : inria-00339316, version 1

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Cherif Smaili, François Charpillet, Maan El Badaoui El Najjar. Multi-sensor Fusion Method Using Bayesian Network for Precise Multi-vehicle Localization. The 11th International IEEE Conference on Intelligent Transportation Systems, Oct 2008, Beijing,, China. IEEE, 2008. 〈inria-00339316〉

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