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A robust and reliable semantic segmentation in adverse weather conditions isvery important for autonomous cars, but most state-of-the-art approaches onlyachieve high accuracy rates in optimal weather conditions. The reason is thatthey are only optimized for good weather conditions and given noise models.However, most of them fail, if data with unknown disturbances occur, and theirperformance decrease enormously. One possibility to still obtain reliableresults is to observe the environment with different sensor types, such ascamera 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 sensordata fusion. Nevertheless, the fusion-based approaches are still susceptible todisturbances and fail to classify disturbed image areas correctly. This problemcan be solved by means of a special training method, the so called RobustLearning Method (RLM), a method by which the neural network learns to handleunknown noise. In this work, two different sensor fusion architectures forsemantic segmentation are compared and evaluated on several datasets.Furthermore, it is shown that the RLM increases the robustness in adverseweather conditions enormously, and achieve good results although no disturbancemodel has been learned by the neural network.

Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion