论文标题
基于CAN-BUS传感器数据的驾驶员识别的机器学习方法
A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data
论文作者
论文摘要
驾驶员识别是控制器区域网络（CAN-BUS）视角中现代装饰车辆的重要领域。许多常规系统用于识别驱动程序。向前一步，大多数研究人员都使用CAN-BUS的传感器数据，但是由于不同模型的车辆方案的变化而存在一些困难。我们的目标是通过基于驾驶行为分析的监督学习算法来识别驾驶员。为了确定驱动程序，提出了一种驾驶员验证技术，该技术使用CAN传感器数据的测量来评估驾驶模式。在本文中，板载诊断（OBD-II）用于捕获CAN-BUS传感器的数据，并根据SAE J1979语句列出了传感器。根据OBD-II的服务，驱动识别是可能的。但是，我们在具有10个驱动程序的完整数据集上获得了两种类型的准确性，并带有两个驱动程序的部分数据集。与越来越多的驾驶员相比，驾驶员数量较少，驱动程序数量较小。与基线算法相比，我们在准确性方面取得了统计学意义的结果
Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm
