A flexible framework for multisensor data fusion using data stream management technologies
André Bolles · 2009
Many applications use sensors to capture an image of the real world, which is needed for automatical processes. E. g. fu-ture driver assistance systems will be based on dynamic in-formation about the car’s environment, the car’s state and the driver’s state. Since there exists no single sensor that can sense the required information, different sensors like radar, video and eye-tracker are used. Typically some provide re-dundant information about the same real world entity, while others measure different things. Thus, the fusion of infor-mation from different sensors is necessary to get a consistant image of the real world. In most sensor fusion systems the sensor configuration is known a priori and the fusion algo-rithms are adapted for these sensor configurations. Thus, changing a sensor fusion system to enable it to process sensor readings from another sensor configuration is hardly possible or completely impossible. Since in development processes of automotive applications different sensor equipment and en-vironmental requirements exist and change frequently a new approach for adapting sensor fusion systems is necessary. Hence, in this work a framework for sensor fusion systems will be developed that allows a flexible adaption of fusion mechanisms. Due to realtime requirements of automotive applications and the flexibility of query processing technolo-gies, data stream management technology will be used to develop a flexible framework for multisensor data fusion.