Modeling of individualitiees in driving through spectral analysis of behavioral signals

Driving behavior modeling using such driving signal, as velocity, following distance, and gas or brake pedal operations, has been investigated for accident prevention and vehicle design. Driving behaviors are different among drivers, and research on driver modeling has also been carried out from different points of view in cognitive and engineering approaches. In this paper, driver's characteristics in driving behaviors are modeled with a Gaussian mixture model (GMM) using "cepstral features" obtained through spectral analysis of gas pedal operation signals. The GMM driver model based on cepstral features is evaluated in driver identification experiments and compared with a conventional GMM driver model that uses raw driving signals without spectral analysis. Experimental results show that the proposed driver model achieves an 89.6% driver identification rate, resulting in 61% error reduction over the conventional driver model.