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by Philippe BONNIFAIT
Intelligent Vehicles are robotic systems that assist the driver in safe and comfortable operation by providing pertinent information or by controlling the vehicle itself. Real-time and safe perception of the driving environment is one of the key issues. In this perception process, global positioning (also called self-localization) and map-matching are useful for retrieving contextual information stored in geographical databases. This talk first recalls the essential attributes of the quality of service of a positioning system. In particular, we will focus on integrity that is nowadays well standardized in the avionic domain (for safety of life reasons). For robotized land vehicles, integrity is a new concern. The concept of Protection Level will be described. The computation of Uncertainty Levels will then be addressed using robust state observation approaches, in a multi-sensor context, since modern vehicles are often equipped with a GPS receiver, dead-reckoning sensors (such as wheel-speed measurements, easily accessible on a CAN bus), road navigable maps, lidars and cameras. In a second part, we will present how to deal with map-matching integrity using multi-hypothesis road tracking. Experimental results obtained with different vehicles in the framework of the POMA/CVIS European project will be presented.