  A robust and reliable semantic segmentation in adverse weather conditions is
very important for autonomous cars, but most state-of-the-art approaches only
achieve high accuracy rates in optimal weather conditions. The reason is that
they are only optimized for good weather conditions and given noise models.
However, most of them fail, if data with unknown disturbances occur, and their
performance decrease enormously. One possibility to still obtain reliable
results is to observe the environment with different sensor types, such as
camera and lidar, and to fuse the sensor data by means of neural networks,
since different sensors behave differently in diverse weather conditions.
Hence, the sensors can complement each other by means of an appropriate sensor
data fusion. Nevertheless, the fusion-based approaches are still susceptible to
disturbances and fail to classify disturbed image areas correctly. This problem
can be solved by means of a special training method, the so called Robust
Learning Method (RLM), a method by which the neural network learns to handle
unknown noise. In this work, two different sensor fusion architectures for
semantic segmentation are compared and evaluated on several datasets.
Furthermore, it is shown that the RLM increases the robustness in adverse
weather conditions enormously, and achieve good results although no disturbance
model has been learned by the neural network.
