HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

A Road-Matching Method for Precise Vehicle Localization using Belief Theory and Kalman Filtering

Maan El Badaoui El Najjar 1 Philippe Bonnifait
1 STF - Systèmes Tolérants aux Fautes
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.
Complete list of metadata

Contributor : Maan El Badaoui El Najjar Connect in order to contact the contributor
Submitted on : Monday, September 17, 2007 - 3:47:15 PM
Last modification on : Wednesday, March 23, 2022 - 3:51:13 PM


  • HAL Id : inria-00172637, version 1



Maan El Badaoui El Najjar, Philippe Bonnifait. A Road-Matching Method for Precise Vehicle Localization using Belief Theory and Kalman Filtering. Autonomous Robots, Springer Verlag, 2005. ⟨inria-00172637⟩



Record views