Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching

Cherif Smaili 1 Maan El Badaoui El Najjar 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper presents a multi-sensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driving-assistance systems. Managing multihypotheses is a useful strategy for the road-matching problem. The multi-sensor fusion and multi-modal estimation are realized using Dynamical Bayesian Network. Experimental results, using data from Antilock Braking System (ABS) sensors, a differential Global Positioning System (GPS) receiver and an accurate digital roadmap, illustrate the performances of this approach, especially in ambiguous situations.
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Communication dans un congrès
19th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2007, Oct 2007, Patras, Greece. IEEE, 6 p., 2007
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https://hal.inria.fr/inria-00170426
Contributeur : Cherif Smaili <>
Soumis le : vendredi 7 septembre 2007 - 16:55:59
Dernière modification le : jeudi 11 janvier 2018 - 06:19:51
Document(s) archivé(s) le : vendredi 9 avril 2010 - 01:46:07

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  • HAL Id : inria-00170426, version 1
  • ARXIV : 0709.1099

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Cherif Smaili, Maan El Badaoui El Najjar, François Charpillet. Multi-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching. 19th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2007, Oct 2007, Patras, Greece. IEEE, 6 p., 2007. 〈inria-00170426〉

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