Detecting objects in the environment is a prerequisite for autonomous driving. To increase the detection performance and robustness of current object detection methods - for example in severe weather - low-level data fusion methods for radar, camera, and lidar data are developed. On a public data set, two of the developed radar-centric low-level fusion methods show higher detection scores than the respective baseline methods not using radar data as part of the input.