Titre :

A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter

Auteur(s) :

Smaili, Cherif [Auteur]
Systèmes Tolérants aux Fautes [STF]
El Badaoui El Najjar, Maan [Auteur]
Systèmes Tolérants aux Fautes [STF]
Charpillet, François [Auteur]
Autonomous intelligent machine [MAIA]

Titre de la revue :

Journal of Intelligent and Robotic Systems

Pagination :

18

Éditeur :

Springer Verlag

Date de publication :

2014-06

ISSN :

0921-0296

Mot(s)-clé(s) en anglais :

Localization · HMM · Switching Kalman Filter · Map-matching · Data fusion · Multi-hypotheses tracking · Intelligent transportation system

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Robotique [cs.RO]

Résumé en anglais : [en]

This paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose ...
Lire la suite >This paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose an approach based on hybrid dynamic bayesian networks enabling to implement in a unified framework two of the most successful families of probabilistic model commonly used for localization: linear Kalman filters and Hidden Markov Models. The combination of these two models enables to manage and manipulate multi-hypotheses and multi-modality of observations characterizing Map Matching problems and it improves integrity approach. Another contribution of the paper is a chained-form state space representation of vehicle evolution which permits to deal with non-linearity of the used odometry model. Experimental results, using data from encoders’ sensors, a DGPS receiver and an accurate digital roadmap, illustrate the performance of this approach, especially in ambiguous situations.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL