Cepstral Analysis of Driving Behavioral Signals for Driver Identification

Spectral analysis is applied to such driving behavioral signals as gas and brake pedal operation signals for extracting drivers´ characteristics while accelerating or decelerating. Cepstral features of each driver obtained through spectral analysis of driving signals are modeled with a Gaussian mixture model (GMM). A GMM driver model based on cepstral features is evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle on a city road. Experimental results show that the driver model based on cepstral features achieves a driver identification rate of 89.6% for driving simulator and 76.8% for real vehicle, resulting in 61 % and 55 % error reduction, respectively, over a conventional driver model that uses raw driving signals without spectral analysis