Semantic radar grids

Abstract

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.