Real-time Driver Identification using Vehicular Big Data and Deep Learning

We propose a driver identification system that uses deep learning technology with controller area network (CAN) data obtained from a vehicle. The data are collected by sensors that are able to obtain the characteristics of drivers. A convolutional neural network (CNN) is used to learn and identify a driver. Various techniques such as CNN 1D, normalization, special section extracting, and post-processing are applied to improve the accuracy of the identification. The experimental results demonstrate that the proposed system achieves an average accuracy of 90% in an experiment with four drivers. In addition, we simulated real-time driver identification in an actual vehicle. In this experiment, we evaluated the time required to reach certain accuracy. For example, the time required to reach an accuracy of 80% was 4-5 min on average.