An unsupervised learning framework for detecting adaptive cruise control operated vehicles in a vehicle trajectory data
An unsupervised learning framework for detecting adaptive cruise control operated vehicles in a vehicle trajectory data
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DOI：
10.1016/j.eswa.2022.118060
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Mohammadreza Khajeh-Hosseini;Alireza Talebpour;Saipraneeth Devunuri;Samer H. Hamdar
中科院分区：
文献类型：
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作者：
Mohammadreza Khajeh-Hosseini;Alireza Talebpour;Saipraneeth Devunuri;Samer H. Hamdar
随着先进驾驶辅助系统（ADAS）的广泛使用，交通动态预计将发生变化。目前，仿真工具被用来捕捉ADAS技术对交通动态的影响。需要收集不同ADAS技术的真实数据，以支持在仿真工具中对这些技术进行真实建模。车辆轨迹是现代交通流理论的基石之一，在驾驶员行为研究和自动驾驶汽车（AV）研究中有着广泛的应用。不幸的是，目前的轨迹数据集未能提供任何关于ADAS技术利用的信息。本研究提出收集和使用一个新的轨迹数据集，其中包含多个使用自适应巡航控制（ACC）的探测器车辆的实例，以确定整个轨迹数据集的ACC类型的行为。由于轨迹数据不是基于ACC利用率进行标记的，因此聚类是将数据集中的相似轨迹安排到同一组中的一种很好的方法。使用此数据集结合聚类，本研究确定了车辆轨迹与使用ACC的车辆具有相似的动力学。
The traffic dynamics are expected to change with the widespread utilization of advanced driver assistance systems (ADAS). Currently, simulation tools are adopted to capture the impacts of ADAS technologies on traffic dynamics. Real-world data collection of different ADAS technologies is required to support realistic modeling of these technologies in simulation tools. Vehicle trajectories are one of the cornerstones of modern traffic flow theory with applications in driver behavior studies and automated vehicle (AV) research. Unfortunately, the current trajectory datasets fail to provide any information on the utilization of ADAS technologies. This study proposes collecting and using a new trajectory dataset that contains multiple instances of probe vehicles using adaptive cruise control (ACC) to identify ACC-type behavior across the entire trajectory dataset. Since the trajectory data is not labeled based on ACC utilization, clustering is an excellent approach to arrange similar trajectories in the dataset into the same group. Using this dataset combined with clustering, this study identifies the vehicle trajectories with similar dynamics to the vehicles using ACC.