Title :
Multi-sensor Fusion Method Using Dynamic Bayesian Network for Precise Vehicle Localization and Road Matching
Author :
Smaili, Cherif ; El Najjar, Maan E. ; Charpillet, François
Author_Institution :
LORIA-INRIA Lorraine - MAIA Team Campus Sci., Nancy
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 multi- hypotheses 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 Anti- lock 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.
Keywords :
Global Positioning System; belief networks; sensor fusion; vehicles; accurate digital roadmap; antilock braking system sensors; differential global positioning system receiver; driving assistance systems; dynamic bayesian network; multimodal estimation; multisensor fusion strategy; road matching method; vehicle localization; Bayesian methods; Databases; Global Positioning System; Intelligent sensors; Intelligent transportation systems; Intelligent vehicles; Navigation; Remotely operated vehicles; Road vehicles; Vehicle dynamics;
Conference_Titel :
Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on
Conference_Location :
Patras
Print_ISBN :
978-0-7695-3015-4
DOI :
10.1109/ICTAI.2007.70
