Estimates of prevalence and risk associated with inattention and distraction based upon in situ naturalistic data.

Thomas A Dingus
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Abstract

By using in situ naturalistic driving data, estimates of prevalence and risk can be made regarding driver populations' secondary task distractions and crash rates. Through metadata analysis, three populations of drivers (i.e., adult light vehicle, teenaged light vehicle, and adult heavy vehicle) were compared regarding frequency of secondary task behavior and the associated risk for safety-critical incidents. Relative risk estimates provide insight into the risk associated with engaging in a single task. When such risk is considered in combination with frequency of use, it sheds additional light on those secondary tasks that create the greatest overall risk to driving safety. The results show that secondary tasks involving manual typing, texting, dialing, reaching for an object, or reading are dangerous for all three populations. Additionally, novice teen drivers have difficulty in several tasks that the other two populations do not, including eating and external distractions. Truck drivers also perform a number of risky "mobile office" types of tasks, including writing, not seen in the other populations. Implications are described for policy makers and designers of in-vehicle and nomadic, portable systems.

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基于现场自然数据的注意力不集中和注意力分散的患病率和风险估计。
通过使用现场自然驾驶数据,可以估计驾驶员群体的次要任务分心和碰撞率的患病率和风险。通过元数据分析,比较了成人轻型车、青少年轻型车和成人重型车三组驾驶员的次要任务行为频率和相关安全关键事件风险。相对风险评估提供了与参与单个任务相关的风险的洞察力。当将这种风险与使用频率结合起来考虑时,它会进一步揭示那些对驾驶安全产生最大总体风险的次要任务。研究结果表明,包括手动打字、发短信、拨号、伸手拿东西或阅读在内的次要任务对这三种人都是危险的。此外,青少年新手司机在一些其他两种人群没有的任务上有困难,包括吃饭和外部干扰。卡车司机还会做一些危险的“移动办公室”类型的工作,包括写作,这在其他人群中是看不到的。本文还描述了车载和移动便携系统对政策制定者和设计者的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Visual and cognitive distraction metrics in the age of the smart phone: A basic review. Dynamics of Driver Distraction: The process of engaging and disengaging. Modeling situation awareness and crash risk. An opportunity for convergence? Understanding the prevalence and risk of distracted driving through the use of crash databases, crash investigations, and other approaches. Estimates of prevalence and risk associated with inattention and distraction based upon in situ naturalistic data.
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