Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings. The predictive performance of any classification model strongly depends on the underlying dataset and the prior knowledge conveyed by the annotated labels. While the labels provide a basis for the learning process, they usually fail to represent inherent relations between the classes - representations, which are a natural element of the human perception system. We propose a training strategy which enables a 3D LiDAR semantic segmentation model to learn structural relationships between the different classes through abstraction. We achieve this by implicitly modeling those relationships through a learning rule for hierarchical multi-label classification (HMC). With a detailed analysis we show, how this training strategy not only improves the model's confidence calibration, but also preserves additional information for downstream tasks like fusion, prediction and planning.

Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its reliability crucial for safety reasons. The active LiDAR sensor is able to create an accurate 3D representation of a scene, making it a valuable addition for environment perception for autonomous vehicles. Due to light scattering and occlusion, the LiDAR's performance change under adverse weather conditions like fog, snow or rain. This limitation recently fostered a large body of research on approaches to alleviate the decrease in perception performance. In this survey, we gathered, analyzed, and discussed different aspects on dealing with adverse weather conditions in LiDAR-based environment perception. We address topics such as the availability of appropriate data, raw point cloud processing and denoising, robust perception algorithms and sensor fusion to mitigate adverse weather induced shortcomings. We furthermore identify the most pressing gaps in the current literature and pinpoint promising research directions.