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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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https://hal.inria.fr/inria-00170426
Contributor : Cherif Smaili Connect in order to contact the contributor
Submitted on : Friday, September 7, 2007 - 4:55:59 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM
Long-term archiving on: : Friday, April 9, 2010 - 1:46:07 AM

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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. 6 p. ⟨inria-00170426⟩

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