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]

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

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 >

Language :

Anglais

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL