Driver modeling based on driving behavior and its evaluation in driver identification
Driver modeling based on driving behavior and its evaluation in driver identification
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DOI：
10.1109/jproc.2006.888405
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发表时间：
2007-02-01
影响因子：
20.6
通讯作者：
Itakura, Fumitada
中科院分区：
文献类型：
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作者：
Miyajima, Chiyomi;Nishiwaki, Yoshihiro;Itakura, Fumitada
所有驾驶员都有驾驶习惯。不同的驾驶员踩油门和制动踏板的方式、转动方向盘的方式以及安全舒适地跟车的跟车距离各不相同。在本文中，我们对跟车和踏板操作模式等驾驶行为进行建模。通过非线性函数近似的最佳速度模型或高斯混合模型（GMM）的统计方法，为每个驾驶员建模映射到二维空间的跟随距离和速度之间的关系。踏板操作模式也用 GMM 建模，GMM 表示原始踏板操作信号的分布或通过原始踏板操作信号的频谱分析提取的频谱特征。使用在驾驶模拟器和真实车辆中收集的驾驶信号在驾驶员识别实验中评估驾驶员模型。实验结果表明，基于踏板操作信号频谱特征的驾驶员模型能够有效模拟驾驶员个体差异，在276名驾驶员的现场测试中识别率达到76.8%，比使用未经频谱分析的原始踏板操作信号的驾驶员模型相对误差降低了55%。
All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.