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Journal Articles Journal of Intelligent and Robotic Systems Year : 2014

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

Abstract

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.
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Dates and versions

hal-01091321 , version 1 (05-12-2014)

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Cite

Cherif Smaili, Maan El Badaoui El Najjar, François Charpillet. A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter. Journal of Intelligent and Robotic Systems, 2014, 74 (3-4), pp.18. ⟨10.1007/s10846-013-9844-4⟩. ⟨hal-01091321⟩
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