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A road matching method for precise vehicle localization using hybrid Bayesian network

Cherif Smaili 1 François Charpillet 1 Maan El Badaoui El Najjar 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 - UMR 9189
Abstract : This article presents a multisensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driver assistance systems. In road navigation, context, integrity, reliability and accuracy are essential qualities for road-matching methods. Particularly, managing multihypotheses is a useful strategy to treat ambiguous situations in the road-matching task. In this study, multisensor fusion and multimodal estimation are realized using a hybrid Bayesian network. To manage multihypothesis, multimodal estimation is proposed. Experimental results, using data from antilock braking system sensors, a differential global positioning system receiver, and an accurate digital roadmap illustrate the performance of the proposed approach, especially in ambiguous situations.
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Contributor : Cherif Smaili <>
Submitted on : Monday, November 17, 2008 - 4:02:53 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM



Cherif Smaili, François Charpillet, Maan El Badaoui El Najjar. A road matching method for precise vehicle localization using hybrid Bayesian network. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2008, 12 (4), pp.176 - 188. ⟨10.1080/15472450802448153⟩. ⟨inria-00339350⟩



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