论文标题
两次通过聚类分析驾驶中的异常检测
Anomaly Detection in Driving by Cluster Analysis Twice
论文作者
论文摘要
偏离驾驶正常交通模式的事件，异常（例如侵略性驾驶或坎bump的道路）可能会损害运输和物流（T＆L）业务的交付效率。因此，检测驾驶异常对T＆L行业至关重要。到目前为止，许多研究都使用了车辆传感器数据来识别异常情况。大多数以前的作品通过使用深度学习或机器学习算法来捕获异常，这些算法需要先前的培训过程和巨大的计算成本。这项研究提出了一种通过群集分析（ADDCAT）驱动驱动中的异常检测方法，该方法将处理的传感器数据簇成不同的物理性质。如果事件永远不适合主要集群，则被认为是一种异常，这被认为是驾驶正常性的模式。该方法提供了一种检测驾驶异常情况的方法，没有先前的培训过程和所需的巨大计算成本。本文验证了该方法在打开数据集上的性能。
Events deviating from normal traffic patterns in driving, anomalies, such as aggressive driving or bumpy roads, may harm delivery efficiency for transportation and logistics (T&L) business. Thus, detecting anomalies in driving is critical for the T&L industry. So far numerous researches have used vehicle sensor data to identify anomalies. Most previous works captured anomalies by using deep learning or machine learning algorithms, which require prior training processes and huge computational costs. This study proposes a method namely Anomaly Detection in Driving by Cluster Analysis Twice (ADDCAT) which clusters the processed sensor data in different physical properties. An event is said to be an anomaly if it never fits with the major cluster, which is considered as the pattern of normality in driving. This method provides a way to detect anomalies in driving with no prior training processes and huge computational costs needed. This paper validated the performance of the method on an open dataset.
