Using Bayesian Programming for Multi-Sensor Multi-Target Tracking in Automotive Applications

Christophe Coué 1 Thierry Fraichard 1 Pierre Bessiere 1 Emmanuel Mazer 1
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Graphisme, Vision et Robotique, 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. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.
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https://hal.archives-ouvertes.fr/hal-00068773
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Submitted on : Saturday, June 17, 2006 - 7:00:02 AM
Last modification on : Monday, November 26, 2018 - 11:52:08 AM
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Christophe Coué, Thierry Fraichard, Pierre Bessiere, Emmanuel Mazer. Using Bayesian Programming for Multi-Sensor Multi-Target Tracking in Automotive Applications. --, 2003, France. 2003. 〈hal-00068773〉

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