Parametric Versus Non-parametric Models of Driving Behavior Signals for Driver Identification
Parametric Versus Non-parametric Models of Driving Behavior Signals for Driver Identification
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DOI：
10.1007/11527923_77
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发表时间：
2005-07
期刊：
影响因子：
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通讯作者：
T. Wakita;K. Ozawa;C. Miyajima;K. Takeda
中科院分区：
文献类型：
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作者：
T. Wakita;K. Ozawa;C. Miyajima;K. Takeda
在本文中，我们提出了一种基于驾驶员跟踪另一辆车时观察到的驾驶行为信号的驾驶员识别方法。使用驾驶模拟器测量驾驶行为信号，例如油门踏板、制动踏板的使用、车速以及与前方车辆的距离。我们比较了使用不同识别模型和不同特征获得的识别率。结果，我们发现非参数模型比参数模型更好。此外，驾驶员的操作信号也优于道路环境信号和汽车行为信号。
In this paper, we propose a driver identification method that is based on the driving behavior signals that are observed while the driver is following another vehicle. Driving behavior signals, such as the use of of the accelerator pedal, brake pedal, vehicle velocity, and distance from the vehicle in front, are measured using a driving simulator. We compared the identification rate obtained using different identification models and different features. As a result, we found the non-parametric models to be better than the parametric models. Also, the driver’s operation signals were found to be better than road environment signals and car behavior signals.