Articles | Volume 17
Adv. Radio Sci., 17, 129–136, 2019
https://doi.org/10.5194/ars-17-129-2019
Adv. Radio Sci., 17, 129–136, 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 et al.

Related authors

Analysis of large-scale UHF-RFID use-cases utilizing full-wave simulation techniques
Miroslav Lach, Christian Looschen, and Erwin Biebl
Adv. Radio Sci., 19, 127–137, https://doi.org/10.5194/ars-19-127-2021,https://doi.org/10.5194/ars-19-127-2021, 2021
Short summary
Road Surface Characteristics for the Automotive 77 GHz Band
Vera Kurz, Hannes Stuelzebach, Florian Pfeiffer, Carlo van Driesten, and Erwin Biebl
Adv. Radio Sci., 19, 165–172, https://doi.org/10.5194/ars-19-165-2021,https://doi.org/10.5194/ars-19-165-2021, 2021
Short summary
Target simulation for UHF RFID DoA estimation systems
Philipp Eschlwech and Erwin Biebl
Adv. Radio Sci., 17, 109–118, https://doi.org/10.5194/ars-17-109-2019,https://doi.org/10.5194/ars-17-109-2019, 2019
Short summary
Inkjet printing of metal nanoparticles for green UHF RFID tags
Bruna Cruz, Andreas Albrecht, Philipp Eschlwech, and Erwin Biebl
Adv. Radio Sci., 17, 119–127, https://doi.org/10.5194/ars-17-119-2019,https://doi.org/10.5194/ars-17-119-2019, 2019
Short summary
Dielectric corner reflectors for mmWave applications
Christian Buchberger, Florian Pfeiffer, and Erwin Biebl
Adv. Radio Sci., 17, 197–203, https://doi.org/10.5194/ars-17-197-2019,https://doi.org/10.5194/ars-17-197-2019, 2019
Short summary

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
Download
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.