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Cluster-Linkage Analysis in Traffic Data Clustering for Development of Advanced Driver Assistance Systems

机译：用于高级驾驶员辅助系统开发的交通数据聚类中的聚类链接分析

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摘要

In terms of vehicle safety, the number of advanced driver assistance systems (ADAS) mounted in an automobile has been increasing recently. For an efficient conceptional design and system validation of ADAS, the representative test scenarios are indispensable. In order to identify the representative scenarios,the real-world traffic scenarios are to be clustered according to their similarity. The hierarchical agglomerative clustering is a well-known method to quantify the similarity of traffic scenarios existing in a database. However, the cluster structure is affected by the linkage criterion used in the agglomerative procedure.This study inquires into the similarity measurement of vehicle-pedestrian near-crashes in the USA. Various linkage criteria are selected to get better understanding of their influence on the clustering results and conduct a comparative study. Furthermore,a hybrid clustering algorithm is presented, which is based on k-covers and k-means clustering. Using the average silhouette width, the optimal number of clusters is calculated and the cluster structures are investigated. In the end, the representative scenarios are selected with the use of centrality measure and form the basis of the scenario catalog making for the reduction of test effort in ADAS development.
机译：在车辆安全方面，最近安装在汽车中的高级驾驶员辅助系统（ADAS）的数量一直在增加。对于ADAS的有效概念设计和系统验证，代表性的测试方案是必不可少的。为了识别代表性场景，将根据现实场景的相似性对它们进行聚类。分层聚集群集是一种众所周知的方法，用于量化数据库中存在的流量场景的相似性。然而，团簇的结构受到凝聚过程中使用的联系准则的影响。本研究探讨了美国车辆行人近崩溃的相似性度量。选择各种链接标准以更好地了解它们对聚类结果的影响并进行比较研究。此外，提出了一种基于k-cover和k-means聚类的混合聚类算法。使用平均轮廓宽度，计算出最佳的聚类数量，并研究聚类结构。最后，使用集中度度量选择具有代表性的方案，并形成方案目录的基础，以减少ADAS开发中的测试工作。

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