Driver identification based on spectral analysis of driving behavioral signals

被引：19

作者：

Nishiwaki, Yoshihiro ^{[1
]}

Ozawa, Koji ^{[1
]}

Wakita, Toshihiro ^{[2
]}

Miyajima, Chiyomi ^{[1
]}

Itou, Katsunobu ^{[1
]}

Takeda, Kazuya ^{[1
]}

机构：

[1] Nagoya Univ, Grad Sch Informat Sci, Nagoya, Aichi 4648603, Japan

[2] Toyoto Cent R&D Labs, Aichi 4801192, Japan

来源：

ADVANCES FOR IN-VEHICLE AND MOBILE SYSTEMS: CHALLENGES FOR INTERNATIONAL STANDARDS | 2007年

关键词：

driving behavior; driver identification; pedal pressure; spectral analysis; Gaussian mixture model;

D O I：

10.1007/978-0-387-45976-9_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this chapter, driver characteristics under driving conditions are extracted through spectral analysis of driving signals. We assume that characteristics of drivers while accelerating or decelerating can be represented by "cepstral features" obtained through spectral analysis of gas and brake pedal pressure readings. Cepstral features of individual drivers can be modeled with a Gaussian mixture model (GMM). Driver models are evaluated in driver identification experiments using driving signals of 276 drivers collected in a real vehicle on city roads. Experimental results show that the driver model based on cepstral features achieves a 76.8 % driver identification rate, resulting in a 55 % error reduction over a conventional driver model that uses raw gas and brake pedal operation signals.

引用

页码：25 / 34

页数：10

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