A New Method for Classification of Hazardous Driver States Based on Vehicle Kinematics and Physiological Signals
A New Method for Classification of Hazardous Driver States Based on Vehicle Kinematics and Physiological Signals
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DOI：
10.1007/978-3-030-11051-2_10
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Mickael Aghajarian;A. Darzi;J. McInroy;D. Novak
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文献类型：
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作者：
Mickael Aghajarian;A. Darzi;J. McInroy;D. Novak
危险驾驶状态是许多交通事故的原因，因此非常需要对这种状态进行准确的检测。这项研究提出了一种新的分类方法，该方法基于之前收集的驾驶数据集进行评估，该数据集包括四种危险驾驶状态的原因的组合：嗜睡、高交通密度、恶劣天气和使用手机。先前的研究包括四次会议和每一次会议中的八个情景。在每个场景中，记录了四个生理信号(例如，心电图)和八个车辆运动学信号(例如，油门、道路偏移)。在以前和现在的研究中，危险驾驶状态的不同原因的存在或不存在都被分类。本文提出了一种基于主成分分析和人工神经网络的分类器。结果表明，所有分类精度都有所提高，特别是在仅使用车辆运动学数据时(平均12.7%)。
Hazardous driver states are the cause of many traffic accidents, and there is therefore a great need for accurate detection of such states. This study proposes a new classification method that is evaluated on a previously collected driving dataset that includes combinations of four causes of hazardous driver states: drowsiness, high traffic density, adverse weather, and cell phone usage. The previous study consisted of four sessions and eight scenarios within each session. Four physiological signals (e.g. electrocardiogram) and eight vehicle kinematics signals (e.g. throttle, road offset) were recorded during each scenario. In both previous and present studies, the presence or absence of the different causes of hazardous driver states was classified. In this study, a new classifier based on principal component analysis and artificial neural networks is proposed. The obtained results show improvement across all classification accuracies, especially when only vehicle kinematics data are used (mean of 12.7%).