Articles | Volume 17
https://doi.org/10.5194/ars-17-129-2019
https://doi.org/10.5194/ars-17-129-2019
19 Sep 2019
 | 19 Sep 2019

A machine learning joint lidar and radar classification system in urban automotive scenarios

Rodrigo Pérez, Falk Schubert, Ralph Rasshofer, and Erwin Biebl

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Cited articles

Chen, V. C., Li, F., Ho, S.-S., and Wechsler, H.: Micro-Doppler effect in radar: phenomenon, model, and simulation study, IEEE T. Aero. Elec. Sys., 42, 2–21, https://doi.org/10.1109/TAES.2006.1603402, 2006. a
European Commission: Traffic Safety Basic Facts on Cyclists, available at: https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/statistics/dacota/bfs2017_cyclists.pdf (last access: 23 November 2018), 2017a.  a
European Commission: Traffic Safety Basic Facts on Pedestrians, available at: https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/statistics/dacota/bfs2017_pedestrians.pdf (last access: 23 November 2018), 2017b. a
Heuel, S. and Rohling, H.: Pedestrian classification in automotive radar systems, 13th International Radar Symposium, 39–44, IEEE, Piscataway, New Jersey, USA, https://doi.org/10.1109/IRS.2012.6233285, 2012. a
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Short summary
This work presents a deep learning approach to classify road users as pedestrians, cyclists or cars using a lidar sensor for detection and a radar sensor for classification. A dataset was gathered on urban roads to train and test the deep learning algorithms. The results show that the system reliably classifies cars, but has trouble with pedestrians and cyclists. The results are improved after aggregating decisions with a Bayes filter. Overlapping targets remain a challenge for the system.