Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Martinez Garcia J, Prophet R, Fuentes MJC, Ebelt R, Vossiek M, Weber I
Publication year: 2019
Language: English


Abstract

We introduce a method to classify ghost moving detections in automotive
radar sensors for advanced driver assistance systems. A fully connected
network is used to distinguish between real and false moving detections
in the occupancy gridmaps. By using this architecture, we combine the
local Doppler information, along with the spatial context of the
surrounding scenario to classify the moving detections. A proof of
concept experiment shows promising results with data from a test drive
in an urban scenario.


FAU Authors / FAU Editors

Ebelt, Randolf
Lehrstuhl für Hochfrequenztechnik
Martinez Garcia, Javier
Lehrstuhl für Hochfrequenztechnik
Prophet, Robert
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik


How to cite

APA:
Martinez Garcia, J., Prophet, R., Fuentes, M.J.C., Ebelt, R., Vossiek, M., & Weber, I. (2019). Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning. In Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2019). Detroit, USA, US.

MLA:
Martinez Garcia, Javier, et al. "Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning." Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2019), Detroit, USA 2019.

BibTeX: 

Last updated on 2019-18-06 at 14:53