Pedestrian Classification for 79 GHz Automotive Radar Systems

Prophet R, Hoffmann M, Ossowska A, Malik W, Sturm C, Vossiek M (2018)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2018

Publisher: IEEE

Event location: Chang Shu CN

DOI: 10.1109/ivs.2018.8500554

Abstract

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.

Authors with CRIS profile

Robert Prophet Lehrstuhl für Hochfrequenztechnik (LHFT) Marcel Hoffmann Lehrstuhl für Hochfrequenztechnik (LHFT) Martin Vossiek Lehrstuhl für Hochfrequenztechnik (LHFT)

Involved external institutions

Valeo Schalter und Sensoren GmbH DE Germany (DE)

How to cite

APA:

Prophet, R., Hoffmann, M., Ossowska, A., Malik, W., Sturm, C., & Vossiek, M. (2018). Pedestrian Classification for 79 GHz Automotive Radar Systems. In Proceedings of the 29th IEEE Intelligent Vehicles Symposium. Chang Shu, CN: IEEE.

MLA:

Prophet, Robert, et al. "Pedestrian Classification for 79 GHz Automotive Radar Systems." Proceedings of the 29th IEEE Intelligent Vehicles Symposium, Chang Shu IEEE, 2018.

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