The near-future deployment of high-level automation vehicles (AVs) can render promising opportunities to solve ongoing hindrances in modern safety-related research. Monitoring fatigued drivers on any road section is one of these challenges. Vehicle trajectory big data, monitored through AVs, include key information with which to monitor fatigued drivers on roads. To mine this upcoming opportunity, a new data-driven approach which allows the direct monitoring of fatigued drivers on road segments is proposed here for the first time. A feasible study was conducted using big vehicle trajectory data and real-life traffic accident data. The results showed that fatigued drivers on a target road section can be successfully surveyed using the driving durations from departure locations to the target road section. It was found that, with a statistical correlation of 0.90, an index for fatigued drivers has strong explanatory power about the traffic accident rate. This finding indicates that the proposed method will be a promising means by which to monitor fatigued drivers at road locations in the upcoming era of autonomous vehicles. In addition, the method is immediately practicable if vehicle trajectory data are available.

中文翻译：

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高度自动化的车辆对疲劳驾驶者进行监控并解释高速公路段交通事故的潜力

高水平自动化车辆（AV）的近期部署可以为解决现代安全相关研究中的持续障碍提供有希望的机会。监控任何路段疲劳的驾驶员是这些挑战之一。通过AV监控的车辆轨迹大数据，包括用于监控道路上疲劳驾驶员的关键信息。为了挖掘这个即将到来的机会，这里首次提出了一种新的数据驱动方法，该方法可以直接监视路段上疲劳的驾驶员。使用大车辆轨迹数据和现实交通事故数据进行了可行性研究。结果表明，使用从出发地点到目标道路段的行驶持续时间，可以成功地调查目标道路段上的疲劳驾驶员。发现，与0.90的统计相关性，疲劳驾驶员的指数对交通事故发生率具有很强的解释力。该发现表明，在即将到来的自动驾驶汽车时代，所提出的方法将是一种有希望的手段，用于监测道路位置的疲劳驾驶员。此外，