论文标题
一种无监督的随机森林聚类技术，用于自动交通情况分类
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization
论文作者
论文摘要
本文介绍了对交通情况分类的随机森林算法的修改。该过程产生了一种无监督的机器学习方法。该算法生成一个包含相似度度量的接近矩阵。然后，将使用层次聚类重新排序该矩阵以获得图形解释的表示。它显示了如何在视觉上解释所得的接近矩阵，以及该方法的元配合仪的变化如何揭示对数据的不同见解。所提出的方法能够从任何数据源群集数据。为了证明方法的潜力，本文使用了从流量模拟中得出的多个功能。 对交通场景集群的了解对于加速验证过程至关重要。该方法的线索是，可以从实际的流量情况自动生成场景模板。这些模板可以在开发过程的各个阶段使用。结果证明该过程非常适合自动分类流量方案。其他应用程序可以从这项工作中受益。
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper. The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work.
