Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVS.2019.8813808 | 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) |
Field | DocType | ISSN |
Radar,Computer vision,Segmentation,Computer science,Automotive radar,3d measurement,Artificial intelligence,Point cloud,Artificial neural network | Conference | 1931-0587 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Prophet | 1 | 0 | 0.68 |
| Gang Li | 2 | 0 | 0.34 |
| Christian Sturm | 3 | 27 | 10.69 |
| Martin Vossiek | 4 | 70 | 14.35 |