A road matching method for precise vehicle ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
A road matching method for precise vehicle localization using hybrid Bayesian network
Author(s) :
Smaili, Cherif [Auteur]
Autonomous intelligent machine [MAIA]
Charpillet, François [Auteur]
Autonomous intelligent machine [MAIA]
El Badaoui El Najjar, Maan [Auteur]
Systèmes Tolérants aux Fautes [STF]
Autonomous intelligent machine [MAIA]
Charpillet, François [Auteur]
Autonomous intelligent machine [MAIA]
El Badaoui El Najjar, Maan [Auteur]

Systèmes Tolérants aux Fautes [STF]
Journal title :
Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
Pages :
176 - 188
Publisher :
Taylor & Francis: STM, Behavioural Science and Public Health Titles
Publication date :
2008
ISSN :
1547-2450
English keyword(s) :
Map Matching
Driver Assistance Systems
Intelligent Transportation Systems
Antilock Braking Systems
Driver Assistance Systems
Intelligent Transportation Systems
Antilock Braking Systems
HAL domain(s) :
Informatique [cs]/Autre [cs.OH]
English abstract : [en]
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, ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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