Adaptions for Automotive Radar Based Occupancy Gridmaps

Beitrag bei einer Tagung

Details zur Publikation

Autor(en): Prophet R, Stark H, Hoffmann M, Sturm C, Vossiek M
Verlag: IEEE
Jahr der Veröffentlichung: 2018
Sprache: Englisch


Environment models are necessary for autonomous driving. The distinction
between drivable and non-drivable underground is elementary. This paper
presents adaptions for radar based occupancy gridmaps, which are a
common representation of the environment. In contrast to standard
occupancy gridmaps or in general standard inverse radar sensor models,
our approach works with velocity dependent parameters and extends free
space calculations. Consequently, the map quality varies less and the
information content of the ego vehicle's immediate vicinity is higher.
Experiments with ground truth data show that the proposed algorithm
produces accurate environment models in urban scenes.

FAU-Autoren / FAU-Herausgeber

Hoffmann, Marcel
Lehrstuhl für Hochfrequenztechnik
Prophet, Robert
Lehrstuhl für Hochfrequenztechnik
Vossiek, Martin Prof. Dr.-Ing.
Lehrstuhl für Hochfrequenztechnik

Autor(en) der externen Einrichtung(en)
Valeo Schalter und Sensoren GmbH


Prophet, R., Stark, H., Hoffmann, M., Sturm, C., & Vossiek, M. (2018). Adaptions for Automotive Radar Based Occupancy Gridmaps. Munich, DE: IEEE.

Prophet, Robert, et al. "Adaptions for Automotive Radar Based Occupancy Gridmaps." Proceedings of the IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2018), Munich IEEE, 2018.


Zuletzt aktualisiert 2019-31-01 um 09:08