@article{oai:nagoya.repo.nii.ac.jp:00007871,
 author = {Miyajima, C. and Nishiwaki, Y. and Ozawa, K. and Wakita, T. and Itou, K. and Takeda, K.},
 journal = {IEEE International Conference on Acoustics, Speech and Signal Processing},
 month = {},
 note = {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.},
 pages = {v--924},
 title = {Cepstral Analysis of Driving Behavioral Signals for Driver Identification},
 volume = {5},
 year = {2006}
}