Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 17
Articles | Volume 17
https://doi.org/10.5194/ars-17-129-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-17-129-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17
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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Latest update: 21 Nov 2024
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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.
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