Semantic Segmentation on Automotive Radar Maps
Prophet R, Li G, Sturm C, Vossiek M (2019)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Event location: Paris
Abstract
As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.
Authors with CRIS profile
Robert Prophet
Lehrstuhl für Hochfrequenztechnik
Gang Li
Lehrstuhl für Hochfrequenztechnik
Martin Vossiek
Lehrstuhl für Hochfrequenztechnik
Involved external institutions
Germany (DE)How to cite
APA:
Prophet, R., Li, G., Sturm, C., & Vossiek, M. (2019). Semantic Segmentation on Automotive Radar Maps. In Proceedings of the IEEE Intelligent Vehicles Symposium. Paris.
MLA:
Prophet, Robert, et al. "Semantic Segmentation on Automotive Radar Maps." Proceedings of the IEEE Intelligent Vehicles Symposium, Paris 2019.
BibTeX: Download