Map matching algorithm using belief function theory

Ghalia Nassreddine, Fahed Abdallah, Thierry Denreux

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)

Abstract

Map matching algorithms are used to integrate an initial estimated position with digital road network data for computing the vehicle position on a road map. In this paper, a map matching algorithm based on belief function theory is proposed. This method provides an accurate estimation of vehicle position relative to a digital road map using belief function theory and interval analysis. The core idea of the proposed algorithm is to handle only interval knowledge acquired from sensors and to use the multiple hypothesis technique. This technique proves to be relevant to treat junction roads situations or parallel roads. The results on simulated and real data show the usefulness of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: 30 Jun 20083 Jul 2008

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Conference

Conference11th International Conference on Information Fusion, FUSION 2008
Country/TerritoryGermany
CityCologne
Period30/06/083/07/08

Keywords

  • Belief function theory
  • Interval analysis
  • Map matching
  • Multiple hypothesis technique
  • Multisensor fusion

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