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Deep Learning-based Object Classification on Automotive Radar Spectra

: Patel, K.; Rambach, K.; Visentin, T.; Rusev, D.; Pfeiffer, M.; Yang, B.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE Radar Conference, RadarConf 2019 : 22-26 April 2019, Westin Waterfront Hotel, Boston, Massachusetts, USA
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-1679-2
ISBN: 978-1-7281-1680-8
Radar Conference (RadarConf) <2019, Boston/Mass.>
Conference Paper
Fraunhofer HHI ()

Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. Automotive radar has shown great potential as a sensor for driver assistance systems due to its robustness to weather and light conditions, but reliable classification of object types in real time has proved to be very challenging. Here we propose a novel concept for radar-based classification, which utilizes the power of modern Deep Learning methods to learn favorable data representations and thereby replaces large parts of the traditional radar signal processing chain. We propose to apply deep Convolutional Neural Networks (CNNs) directly to regions-of-interest (ROI) in the radar spectrum and thereby achieve an accurate classification of different objects in a scene. Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. We identify deep learning challenges that are specific to radar classification and introduce a set of novel mechanisms that lead to significant improvements in object classification performance compared to simpler classifiers. Our results demonstrate that Deep Learning methods can greatly augment the classification capabilities of automotive radar sensors.