Radar has a long tradition in driver assistance systems. One of its advantages is its robustness against certain light and weather conditions. It is usually used to track surrounding objects and to detect objects which can be utilized for assistance functions, e.g. for emergency braking. However for autonomous driving, a deeper understanding of the vehicle's surroundings is necessary. In this paper we show that semantic knowledge can be obtained from a radar grid by classifying the containing objects on the cell level. This allows for omitting a prior object extraction and results directly in a semantic radar grid. We show on a data set recorded with four radar sensors that this approach produces good results distinguishing cars and other objects.
Semantic radar grids
2017 IEEE Intelligent Vehicles Symposium (IV) ; 1170-1175
2017-06-01
1038119 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Dual Inverse Sensor Model for Radar Occupancy Grids
IEEE | 2019
|SEMANTIC SEGMENTATION ON AUTOMOTIVE RADAR MAPS
British Library Conference Proceedings | 2019
|