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使用自然驾驶数据评估驾驶员碰撞风险因素及发生率

Driver crash risk factors and prevalence evaluation using naturalistic driving data.

作者信息

Dingus Thomas A, Guo Feng, Lee Suzie, Antin Jonathan F, Perez Miguel, Buchanan-King Mindy, Hankey Jonathan

机构信息

Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061;

Virginia Tech Transportation Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061; Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061.

出版信息

Proc Natl Acad Sci U S A. 2016 Mar 8;113(10):2636-41. doi: 10.1073/pnas.1513271113. Epub 2016 Feb 22.


DOI:10.1073/pnas.1513271113
PMID:26903657
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4790996/
Abstract

The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.

摘要

对撞车因果因素的准确评估可为有效的交通政策、车辆设计和驾驶员教育提供基础信息。通过多个车载摄像机和传感器收集的自然istic驾驶(ND)数据为评估撞车前几秒内的风险因素提供了独特的机会。本文使用了美国国家科学院赞助的一个ND数据集,该数据集包含905起造成人员受伤和财产损失的撞车事件,就我们所知,其规模允许首次仅使用撞车事件对因果因素进行直接分析。结果表明,近年来撞车原因发生了巨大变化,近90%的撞车事故中存在与驾驶员相关的因素(即失误、损伤、疲劳和注意力分散)。结果还明确表明,注意力分散对驾驶员安全有害,手持电子设备的使用率和风险都很高。

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Driver crash risk factors and prevalence evaluation using naturalistic driving data.

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