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Incorporation of Driver Distraction in Car-following model based on Driver's Eye Glance Behavior

机译：基于驾驶员视线行为的驾驶员跟进模型中的驾驶员分心

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This paper aims to incorporate driver distraction into car-following model to demonstrate hazardous situations such as vehicle collision. Many reports point out a driver distraction as one of the major causes of automobile collisions and driver's eye glance behavior is representative indicator for quantitatively evaluating distraction. To analyze distraction, a 100-car Naturalistic Driving Study data including eye glance data is used. This study classified several variables affecting the glance behavior into two different groups and analyzed in different ways. Based on the results of the analysis, decision tree analysis is conducted to derive driving scenarios according to the eye glance behavior and total of eleven scenarios are derived. Driver's glance behavior is modelled by scenarios and incorporated into the existing car-following model. As one of the existing car-following model, Oversaturated Freeway Flow Algorithm (OFFA) is extended to distracted OFFA. We show that distracted OFFA describes real-world driving more closely in terms of vehicle safety by comparing a distribution of time to collision (TTC) with existing car-following models.
机译：本文旨在将驾驶员的注意力转移到跟车模型中，以演示危险情况，例如车辆碰撞。许多报告指出，驾驶员分心是汽车碰撞的主要原因之一，驾驶员的目光行为是定量评估驾驶员分心的代表指标。为了分析干扰，我们使用了100辆自然驾驶研究数据，其中包括眼神数据。这项研究将影响扫视行为的几个变量分为两个不同的组，并以不同的方式进行了分析。根据分析结果，进行决策树分析，以根据眼动行为推导出驾驶场景，并推导出总共11种场景。驾驶员的视线行为通过场景进行建模，并整合到现有的跟车模型中。作为现有的跟车模型之一，过饱和高速公路流算法（OFFA）扩展为分散的OFFA。我们通过将碰撞时间（TTC）的分布与现有的跟车模型进行比较，证明了分散注意力的OFFA在车辆安全方面更真实地描述了现实世界中的驾驶。

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