Pedestrian Classification for 79 GHz Automotive Radar Systems

Radar sensors have become an integral part of advanced driver assistance systems. Merely detecting targets will not, however, advance their contribution. Rather, an object classification capability is required to distinguish vulnerable road users from other objects, such as vehicles. To achieve this, targets that are determined from the range-Doppler-Matrix created by a 79 GHz chirp sequence radar are clustered to objects. Different classifiers then use previously calculated characteristic features of moving objects to generate the object classes “Pedestrian” and “Other”. As a result, the success rate of one measurement reaches up to 95.3% for well-suited classifiers and a bandwidth of 1.6 GHz. Moreover, the robustness of the classification process is increased by tracking the objects. The proposed algorithm for pedestrian classification is not only faster than conventional approaches using micro-Doppler signatures, but also requires less computational effort. Implemented in vehicles, this can be a major contribution to protect vulnerable road users such as pedestrians.