Safety-critical applications such as autonomous driving require robust 3D environment perception algorithms capable of handling diverse and ambiguous surroundings. The predictive performance of classification models is heavily influenced by the dataset and the prior knowledge provided by the annotated labels. While labels guide the learning process, they often fail to capture the inherent relationships between classes that are naturally understood by humans. We propose a training strategy for a 3D LiDAR semantic segmentation model that learns structural relationships between classes through abstraction. This is achieved by implicitly modeling these relationships using a learning rule for hierarchical multi-label classification (HMC). Our detailed analysis demonstrates that this training strategy not only improves the model's confidence calibration but also retains additional information useful for downstream tasks such as fusion, prediction, and planning.

翻译：自动驾驶等安全关键应用需要能够处理多样且模糊环境的鲁棒性3D环境感知算法。分类模型的预测性能在很大程度上受数据集和标注标签所提供的先验知识影响。虽然标签能够指导学习过程，却往往无法捕捉人类自然理解的类别间内在关联。本文提出一种用于3D激光雷达语义分割模型的训练策略，该策略通过抽象化学习类别间的结构关系。这是通过使用层次化多标签分类（HMC）的学习规则隐式建模这些关系实现的。我们的详细分析表明，该训练策略不仅能提升模型的置信度校准能力，还能保留对融合、预测与规划等下游任务有用的附加信息。