This Car is Mine!: Driver Pattern Dataset extracted from CAN-bus

We propose a driver pattern dataset consists of 51 features extracted from CAN (Controller Area Network) of Hyundai YF Sonata while four drivers drove city roads of Seoul, Republic of Korea. Under the belief that different driving patterns implicitly exist at CAN data, we collected CAN diagnosis data from four drivers in pursuit of research on driver identification, driver profiling, and abnormal driving behavior detection. Four drivers are named A, B, C, and D. The driver A performed eight trips, the driver B performed eight trips, the driver C performed five trips, and the driver D performed nine trips. We collected 51 features from CAN utilizing On-Board Diagnostic 2 (OBD-II) and CarbigsP as a scanning tool to extract features. Every feature is recorded by 1 second along with the trip. Note that the time consumed by each trip is all different as the traffic environment were all different during the data collection. In our research work 'This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks', we showed the dataset could be fully utilized to analyze the unique characteristics of each driver. We expect this driver pattern dataset to be a concrete baseline dataset for future studies on novel driving pattern recognition approaches.

本研究提出了一种驾驶模式数据集，该数据集由51个特征组成，这些特征来源于现代悦动（Hyundai YF Sonata）的控制器局域网络（CAN），数据采集于韩国首尔市区道路，由四位驾驶员驾驶。基于对CAN数据中隐含不同驾驶模式的信念，本研究收集了四位驾驶员的CAN诊断数据，旨在研究驾驶员识别、驾驶员画像构建及异常驾驶行为检测。四位驾驶员分别命名为A、B、C和D。驾驶员A完成了八次行程，驾驶员B完成了八次行程，驾驶员C完成了五次行程，驾驶员D完成了九次行程。通过车载诊断系统2（OBD-II）和CarbigsP扫描工具，我们从CAN中提取了51个特征。每个特征均与行程同步记录，时间间隔为1秒。请注意，每次行程所耗时间均不相同，这是因为数据采集期间交通环境各异。在研究工作《此车归我！利用生成对抗网络进行驾驶员识别的汽车盗窃防范措施》中，我们展示了该数据集能够被充分用于分析每位驾驶员的独特特征。我们期望该驾驶模式数据集将成为未来关于新颖驾驶模式识别方法研究的一个具体基准数据集。