Multi level fusion with confidence measures for automotive safety applications

The fusion of data from different sensorial sources is today the most promising method to increase robustness and reliability of environmental perception. The paper presents an approach for using fuzzy operators for the hierarchical fusion of processing results in a multi sensor data processing system for the detection of vehicles in road environments. Tracking and fusion of intermediate results is performed in several levels of processing (signal level, several feature levels, object level). To produce higher level hypotheses on the basis of lower level components, grouping rules using certain assignment decisions are used. In this paper this is seen as a classification procedure that is testing and assigning components step by step to a higher level feature or object. For these classifications a suitable combination of a fuzzy operator for fusion and membership functions for classification is proposed to meet the requirements of the hierarchical classification and the necessity to include confidence values for that. An example is given for the fusion of image and radar data in a vehicle detection algorithm.