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Modèle bayésien pour l'analyse multimodale d'environnementsdynamiques et encombrés : Application à l'assistance à la conduite en milieu urbain

Christophe Coue 1
1 SHARP - Automatic Programming and Decisional Systems in Robotics
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. In particular, target tracking is still challenging in urban traffic situations, because of the large number of rapidly maneuvering targets. The goal of this thesis is to present an original way to perform target position and velocity estimation, based on the occupancy grid framework. The main interest of this method is to avoid the decision problem of classical multi-target tracking algorithms. Obtained occupancy grids are then combined with danger estimation to perform an elementary task of obstacle avoidance with an electric car.
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Contributor : Christophe Coue <>
Submitted on : Monday, April 5, 2004 - 9:46:59 AM
Last modification on : Monday, December 28, 2020 - 3:44:01 PM
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Christophe Coue. Modèle bayésien pour l'analyse multimodale d'environnementsdynamiques et encombrés : Application à l'assistance à la conduite en milieu urbain. Modélisation et simulation. Institut National Polytechnique de Grenoble - INPG, 2003. Français. ⟨tel-00005527⟩

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