  Automotive traffic scenes are complex due to the variety of possible
scenarios, objects, and weather conditions that need to be handled. In contrast
to more constrained environments, such as automated underground trains,
automotive perception systems cannot be tailored to a narrow field of specific
tasks but must handle an ever-changing environment with unforeseen events. As
currently no single sensor is able to reliably perceive all relevant activity
in the surroundings, sensor data fusion is applied to perceive as much
information as possible. Data fusion of different sensors and sensor modalities
on a low abstraction level enables the compensation of sensor weaknesses and
misdetections among the sensors before the information-rich sensor data are
compressed and thereby information is lost after a sensor-individual object
detection. This paper develops a low-level sensor fusion network for 3D object
detection, which fuses lidar, camera, and radar data. The fusion network is
trained and evaluated on the nuScenes data set. On the test set, fusion of
radar data increases the resulting AP (Average Precision) detection score by
about 5.1% in comparison to the baseline lidar network. The radar sensor fusion
proves especially beneficial in inclement conditions such as rain and night
scenes. Fusing additional camera data contributes positively only in
conjunction with the radar fusion, which shows that interdependencies of the
sensors are important for the detection result. Additionally, the paper
proposes a novel loss to handle the discontinuity of a simple yaw
representation for object detection. Our updated loss increases the detection
and orientation estimation performance for all sensor input configurations. The
code for this research has been made available on GitHub.
