Driver Identification with Time and Frequency Features Derived from Vehicular Acceleration Data

With the increase of the related facilities and services around the driver, the driver’s identity becomes more and more important, so the research on the driver’s identification is also increasing gradually. Since the emergence of the online car-hailing platform represented by Uber, people’s travel has become more and more convenient, but there have also been many problems surrounding the identity of the driver. For example, the actual information of the driver does not match the information registered on the platform, which increase safety risk for passengers. In this paper, we propose a novel driver identification scheme that first converts the raw data of the x and y axes of the accelerometer into feature vectors by a novel data transformation method which adds frequency domain perspective on the basis of time domain perspective, adopts sliding window and fast Fourier transform and then uses these feature vectors as the input of neural network. Finally, we identify the driver through our designed driver identification algorithm, which accepts as input the probability distribution of the network output. In our experiments, we experiment with 10 drivers and use accuracy, precision, and recall as outcome metrics. Experimental results show that the performance based on time and frequency features is better than that of time or frequency features alone.