| Paper: | MLSP-P6.12 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Other Applications | ||
| Title: | Cepstral Analysis of Driving Behavioral Signals for Driver Identification | ||
| Authors: | Chiyomi Miyajima, Yoshihiro Nishiwaki, Koji Ozawa, Toshihiro Wakita, Katsunobu Itou, Kazuya Takeda, Nagoya University, Japan | ||
| Abstract: | 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 are modeled with a Gaussian mixture model. The driver model 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 an identification rate of 89.6% for driving simulator and 76.8% for real vehicle, resulting in 61% and 55% error reduction, respectively, over a driver model that uses raw driving signals without spectral analysis. | ||
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