Abstract
In this paper the multi sensor fusion results obtained within the European research project GEODE are presented. The layout of the test lane and the individual sensors used are described. The implementation of the SCOOP algorithm improves the ROC curves, as the false alarm surface and the number of false alarms both are taken into account. The confidence grids, as produced by the sensor manufacturers, of the sensors are used as input for the different sensor fusion methods implemented. The multisensor fusion methods implemented are Bayes, Dempster-Shafer, fuzzy probabilities and rules. The mapping of the confidence grids to the input parameters for fusion methods is an important step. Due to limited amount of the available data the entire test lane is used for training and evaluation. All four sensor fusion methods provide better detection results than the individual sensors.
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