Malzer Claudia, Baum Marcus
Data Fusion Group, Institute of Computer Science, University of Göttingen, 37077 Göttingen, Germany.
Faculty of Engineering and Health, HAWK University of Applied Sciences and Arts Hildesheim/Holzminden/Göttingen, 37085 Göttingen, Germany.
Sensors (Basel). 2021 May 13;21(10):3410. doi: 10.3390/s21103410.
High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.
高分辨率汽车雷达传感器在交通场景中对移动物体的检测、分类和跟踪方面发挥着越来越重要的作用。在此背景下，聚类常用于对检测点进行分组。然而，由于不同扫描中可用数据点的数量和密度存在差异，这是一项特别具有挑战性的任务。到目前为止，大多使用基于密度的聚类方法DBSCAN的改进版本，而分层方法很少被考虑。在本文中，我们探索了分层DBSCAN变体HDBSCAN在聚类雷达测量数据方面的适用性。为了改进其无监督版本所取得的结果，我们建议基于来自聚类候选的聚合背景信息使用聚类级约束。此外，我们建议应用距离阈值以避免在低层次级别选择小聚类。基于公开可用的自动驾驶数据集nuScenes中的示例性交通场景，我们将基于约束的方法与其他方法（包括基于标签的半监督HDBSCAN）一起进行测试。我们的实验表明，聚类级约束有助于使HDBSCAN适应给定的应用场景，因此可以比无监督方法取得显著更好的结果。然而，该方法需要精心选择约束标准，而在不断变化的环境中可能难以选择。
