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

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dc.creatorSmaili, Cherif
dc.creatorNajjar, Maan El Badaoui El
dc.creatorCharpillet, François
dc.date2007-09-07
dc.date.accessioned2026-07-07T08:28:12Z
dc.date.available2026-07-07T08:28:12Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/0709.1099
dc.identifierhttp://arxiv.org/abs/0709.1099
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/137531
dc.subjectArtificial Intelligence
dc.subjectRobotics
dc.titleMulti-Sensor Fusion Method using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching
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