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

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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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Authors and Affiliations

  1. Département Informatique de l’université U.S.T.H.B, 32 El Alia, Bab Ezzouar Alger, 16111, Algerie, France

    Cherif Smaili

  2. LAGIS-CNRS UMR 8219 Université de Lille 1, Avenue Paul Langevevin, 59650, Villeneuve D’Ascq Cedex, France

    Maan El Badaoui El Najjar

  3. MAIA Team, INRIA Nancy Grand EST, Loria Campus Scientifique, BP 239, 54506, Vandoeuvre-lès-Nancy, France

    François Charpillet

Authors
  1. Cherif Smaili
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  2. Maan El Badaoui El Najjar
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  3. François Charpillet
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Correspondence to Maan El Badaoui El Najjar.

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Smaili, C., Najjar, M.E.B.E. & Charpillet, F. A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter. J Intell Robot Syst 74, 725–743 (2014). https://doi.org/10.1007/s10846-013-9844-4

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  • DOI: https://doi.org/10.1007/s10846-013-9844-4

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