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.

Viewed

Total article views: 1,188 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
763 392 33 1,188 34 34
  • HTML: 763
  • PDF: 392
  • XML: 33
  • Total: 1,188
  • BibTeX: 34
  • EndNote: 34
Views and downloads (calculated since 19 Sep 2019)
Cumulative views and downloads (calculated since 19 Sep 2019)

Viewed (geographical distribution)

Total article views: 1,004 (including HTML, PDF, and XML) Thereof 1,002 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Sep 2022
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.