A Hybrid Bayesian Framework for Map Matching: ...
Document type :
Article dans une revue scientifique
Title :
A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter
Author(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]
Systèmes Tolérants aux Fautes [STF]
El Badaoui El Najjar, Maan [Auteur]

Systèmes Tolérants aux Fautes [STF]
Charpillet, François [Auteur]
Autonomous intelligent machine [MAIA]
Journal title :
Journal of Intelligent and Robotic Systems
Pages :
18
Publisher :
Springer Verlag
Publication date :
2014-06
ISSN :
0921-0296
English keyword(s) :
Localization · HMM · Switching Kalman Filter · Map-matching · Data fusion · Multi-hypotheses tracking · Intelligent transportation system
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Robotique [cs.RO]
Informatique [cs]/Robotique [cs.RO]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :