Biometric identification using driving behavioral signals

In this paper we investigate the uniqueness of driver behavior in vehicles and the possibility to use it for personal identification with the objectives to achieve safer driving, to assist the driver in case of emergencies, and to be a part of multi-mode biometric signature for driver identification. We use Gaussian Mixture Models (GMM) for modeling the individualities of the accelerator and brake pedal pressures, and focus on not only the static features, but also the dynamics of the pedal pressures. Experimental results show that the dynamic features significantly improve the performance of driver identification.