An Experimental Study Towards Driver Identification for Intelligent and Connected Vehicles

With the continuous expansion and deepening of Intelligent and Connected Vehicles (ICVs), advanced technology continues to emerge, making ICVs more intelligent to provide services for drivers and protect them. It is noticed that the emergence of almost all advanced technologies is based on the use of automotive data. Using automobile data can not only restore the current driving state, but also realize the identification of the driver. Different from previous works about driver identification, we don't use any manufacturer's Controller Area Network (CAN) protocol to parse vehicle's data and don't use any external sensor data. We first rely on the broadcast feature of the CAN bus, and use the automotive diagnostic tool to get all real-time data from the On-Board Diagnostic (OBD-II) port. Then, we use Feature scaling and Principal Component Analysis (PCA) algorithm to preprocess the data. Finally, we use k-Nearest Neighbor (k-NN) algorithm and Naive Bayes algorithm combined with voting mechanism to successfully identify the driver's identity. The experimental results show that the recognition rate of ten drivers is 100%.