当前位置: X-MOL 学术Journal of Safety Research › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Determining the risk of driver-at-fault events associated with common distraction types using naturalistic driving data
Journal of Safety Research ( IF 4.264 ) Pub Date : 2021-08-17 , DOI: 10.1016/j.jsr.2021.08.003
Ou Stella Liang 1 , Christopher C Yang 1
Affiliation  

Introduction: Studies thus far have focused on automobile accidents that involve driver distraction. However, it is hard to discern whether distraction played a role if fault designation is missing because an accident could be caused by an unexpected external event over which the driver has no control. This study seeks to determine the effect of distraction in driver-at-fault events. Method: Two generalized linear mixed models, one with at-fault safety critical events (SCE) and the other with all-cause SCEs as the outcomes, were developed to compare the odds associated with common distraction types using data from the SHRP2 naturalistic driving study. Results: Adjusting for environment and driver variation, 6 of 10 common distraction types significantly increased the risk of at-fault SCEs by 20-1330%. The three most hazardous sources of distraction were handling in-cabin objects (OR = 14.3), mobile device use (OR = 2.4), and external distraction (OR = 1.8). Mobile device use and external distraction were also among the most commonly occurring distraction types (10.1% and 11.0%, respectively). Conclusions: Focusing on at-fault events improves our understanding of the role of distraction in potentially avoidable automobile accidents. The in-cabin distraction that requires eye-hand coordination presents the most danger to drivers’ ability in maintaining fault-free, safe driving. Practical Applications: The high risk of at-fault SCEs associated with in-cabin distraction should motivate the smart design of the interior and in-vehicle information system that requires less visual attention and manual effort.



中文翻译:

使用自然驾驶数据确定与常见分心类型相关的驾驶员过失事件的风险

简介:迄今为止,研究主要集中在涉及驾驶员分心的汽车事故上。但是,如果缺少故障指示,则很难辨别分心是否起作用,因为事故可能是由驾驶员无法控制的意外外部事件引起的。本研究旨在确定分心在驾驶员过错事件中的影响。方法:开发了两种广义线性混合模型,一种以故障安全关键事件 (SCE) 为结果,另一种以全因 SCE 作为结果,使用来自 SHRP2 自然驾驶研究的数据比较与常见分心类型相关的几率. 结果:调整环境和驾驶员变化后,10 种常见的分心类型中有 6 种显着增加了故障 SCE 的风险 20-1330%。三个最危险的分心来源是处理舱内物品 (OR = 14.3)、使用移动设备 (OR = 2.4) 和外部分心 (OR = 1.8)。移动设备使用和外部分心也是最常见的分心类型(分别为 10.1% 和 11.0%)。结论:关注故障事件可以提高我们对分心在可能避免的汽车事故中的作用的理解。需要手眼协调的车内分心对驾驶员保持无故障、安全驾驶的能力构成最大威胁。实际应用:与车内分心相关的故障 SCE 的高风险应该会激发内部和车载信息系统的智能设计,这需要更少的视觉注意力和手动操作。

更新日期:2021-08-17
down
wechat
bug