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.
The dataset contains 51 features extracted from CAN along with numerous trips performed by four drivers. The four drivers drove along city roads of Seoul, the Republic of Korea. The recorded 51 features can be employed for driver identification, driver profiling, abnormal driving pattern identification, and any related tasks. Please check the abstract for a more detailed description.
Directory A, B, C and D contains .csv files of CAN data. Each .csv file represents a trip.
The names of 51 features are described in the features.pkl file. Please check the file for detailed information.
Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks." arXiv preprint arXiv:1911.09870 (2019).
Park, Kyung Ho, and Huy Kang Kim. "This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks.", ESCAR Asia (2019)
The study was funded by Institute for Information and communications Technology Promotion (Grant No. 2020-0-00374, Development of Security Primitives for Unmanned Vehicles).