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A prerequisite to the design of future advanced driver assistance systems for cars is a sensing system that provides 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 combines different sensors (laser, radar and video) and relies 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 initially to design robot control programs but its scope of application including uncertain or incomplete knowledge handling problems.