  A good and robust sensor data fusion in diverse weather conditions is a quite
challenging task. There are several fusion architectures in the literature,
e.g. the sensor data can be fused right at the beginning (Early Fusion), or
they can be first processed separately and then concatenated later (Late
Fusion). In this work, different fusion architectures are compared and
evaluated by means of object detection tasks, in which the goal is to recognize
and localize predefined objects in a stream of data. Usually, state-of-the-art
object detectors based on neural networks are highly optimized for good weather
conditions, since the well-known benchmarks only consist of sensor data
recorded in optimal weather conditions. Therefore, the performance of these
approaches decreases enormously or even fails in adverse weather conditions. In
this work, different sensor fusion architectures are compared for good and
adverse weather conditions for finding the optimal fusion architecture for
diverse weather situations. A new training strategy is also introduced such
that the performance of the object detector is greatly enhanced in adverse
weather scenarios or if a sensor fails. Furthermore, the paper responds to the
question if the detection accuracy can be increased further by providing the
neural network with a-priori knowledge such as the spatial calibration of the
sensors.
