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

Viewed

Total article views: 1,546 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,018 486 42 1,546 48 46
  • HTML: 1,018
  • PDF: 486
  • XML: 42
  • Total: 1,546
  • BibTeX: 48
  • EndNote: 46
Views and downloads (calculated since 19 Sep 2019)
Cumulative views and downloads (calculated since 19 Sep 2019)

Viewed (geographical distribution)

Total article views: 1,352 (including HTML, PDF, and XML) Thereof 1,350 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 2024
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.
Read more