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.

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.

This paper describes how a mobile robot (a six-wheeled Koala equipped with a PAL pan-tilt camera) can chase an elusive target (a remote controlled toy car) in a unknown and unconstrained environment. First, the paper demonstrates the efficiency, simplicity, and adequacy of Bayesian robot programming to quickly develop such applications. Next, it illustrates that a high information compression ratio may be obtained by some pertinent sensory-motor decoupling.