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.

翻译：在自动驾驶等安全关键应用中，需要能够应对高度多样性和模糊环境的鲁棒3D环境感知算法。任何分类模型的预测性能都强烈依赖于底层数据集以及标注标签所传达的先验知识。虽然标签为学习过程提供了基础，但它们通常无法表示类别之间的内在关系——而这类表征是人类感知系统的自然要素。我们提出一种训练策略，使3D LiDAR语义分割模型能够通过抽象学习不同类别之间的结构关系。我们通过为层次化多标签分类（HMC）设计的学习规则隐式建模这些关系来实现这一点。通过详细分析，我们展示了这种训练策略不仅改善了模型的置信度校准，还为融合、预测和规划等下游任务保留了额外信息。