Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, TN, United States.
Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, TN, United States.
Accid Anal Prev. 2020 Oct;146:105733. doi: 10.1016/j.aap.2020.105733. Epub 2020 Sep 9.
Distracted and impaired driving is a key contributing factor in crashes, leading to about 35% of all transportation-related deaths in recent years. Along these lines, cognitive issues like inattentiveness can further increase the chances of crash involvement. Despite its prevalence and importance, little is known about how the duration of these distractions is associated with critical events, such as crashes or near-crashes. With new sensors and increasing computational resources, it is possible to monitor drivers, vehicle performance, and roadway features to extract useful information, e.g., eyes off the road, indicating distraction and inattention. Using high-resolution microscopic SHRP2 naturalistic driving data, this study conducts in-depth analysis of both impairments and distractions. The data has more than 2 million seconds of observations in 7394 baselines (no event), 1228 near-crashes, and 617 crashes. The event data was processed and linked with driver behavior and roadway factors. The intervals of distracted driving during the period of observation (15 seconds) were extracted; next, rigorous fixed and random parameter logistic regression models of crash/near-crash risk were estimated. The results reveal that alcohol and drug impairment is associated with a substantial increase in crash/near-crash event involvement of 34%, and the highest correlations with crash risk include duration of distraction through dialing on a cellphone, texting while driving, and reaching for an object. Using detailed pre-crash data from instrumented vehicles, the study contributes by quantifying crash risk vis-à-vis detailed driving impairment and information on secondary task involvement, and discusses the implications of the results.
分心和驾驶障碍是导致事故的主要因素之一，近年来导致约 35%的与交通相关的死亡。在这方面，注意力不集中等认知问题会进一步增加事故发生的可能性。尽管分心驾驶很普遍且很重要，但人们对这些分心的持续时间与关键事件（如事故或险些发生的事故）之间的关联知之甚少。随着新传感器和不断增加的计算资源，可以监控驾驶员、车辆性能和道路特征以提取有用信息，例如眼睛离开道路，表明分心和注意力不集中。利用高分辨率微观 SHRP2 自然驾驶数据，本研究对损伤和分心进行了深入分析。该数据在 7394 个基线（无事件）、1228 个接近事故和 617 个事故中观察了超过 200 万秒。对事件数据进行了处理，并与驾驶员行为和道路因素相关联。提取了观察期内（15 秒）分心驾驶的间隔；接下来，估计了严格的固定和随机参数逻辑回归模型的碰撞/接近碰撞风险。结果表明，酒精和药物损伤与碰撞/接近碰撞事件参与度的大幅增加有关，增加了 34%，与碰撞风险相关性最高的包括通过拨打电话、驾驶时发短信和伸手拿东西导致的分心时间。利用仪器化车辆的详细预碰撞数据，本研究通过量化与详细驾驶损伤和次要任务参与度有关的碰撞风险做出了贡献，并讨论了结果的意义。
