Determining critical skill deficits that predict novice driver crashes: A prospective study using a novel virtual driving test at point of licensure, state licensing data and crash outcomes

确定预测新手驾驶员碰撞事故的关键技能缺陷：一项前瞻性研究，在驾驶执照、州驾驶数据和碰撞结果时使用新型虚拟驾驶测试

• 批准号: 10000116
• 负责人: FLAURA K WINSTON
• 金额: $ 21.03万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-22 至 2021-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10000116
• 关键词: Address; Adolescence; Adolescent; Adopted; Affect; Age; Automobile Driving; Behavior; Cognitive; Data; Data Set; Data Sources; Databases; Development; Foundations; Future; Goals; Grant; Health; Incidence; Individual Differences; Industry; Injury; Intervention; Laboratories; Lead; Licensing; Licensure; Link; Machine Learning; Measures; Methodology; Ohio; Outcome; Police; Population; Population Study; Populations at Risk; Prospective Studies; Proxy; Public Health; Reporting; Research; Research Support; Risk; Risk Factors; Rural; Safety; Sampling; Science; Site; Speed; System; Techniques; Technology; Teenagers; Test Result; Testing; Time; Time Factors; Training; Universities; Validation; Vehicle crash; Young driver; base; cognitive development; design; driving skills; experience; improved; innovation; interest; mortality; novel; personalized intervention; programs; prospective; safety study; sex; skill acquisition; skills; suburb; virtual

Project Summary Motor vehicle crashes lead causes of adolescent mortality, making this a major health threat facing US adolescents. Given that 95.6% of novice driver crashes are due to driver error, improved skill should reduce crash incidence. Research supports this: novice driver crash rates peak immediately following licensure and decline steeply with experience gained over the months following licensure. However, it is not known which critical driving skill deficits present at licensure predict crashes. The long-term goal of this research is to develop interventions prior to licensure that can lead to the safest independent driving post licensure. With this R21 project, we will identify deficits in driving skills that are (a) associated with major predictors of early crash risk (age at licensure and sex) and that (b) can predict differences in crash rates within the first year of independent licensure. We will leverage our exclusive access to an innovative, newly available data source for young driver research. To enable study of safety critical driving skill deficits in novice drivers, results from a new (as of July 2017) virtual driving test (VDT) - developed by the PI, adopted by the State of Ohio and delivered immediately prior to the on-road licensing examination - will be linked to Ohio licensing and crash data. We propose these aims: Aim 1: identify skill clusters using multivariate clustering, regression and machine learning techniques to test the hypothesis that these clusters are related to age at licensure, sex, and time spent in the learner period; Aim 2a: use data on driving skills linked with reported crash data in Ohio to determine whether the skill clusters identified in Aim 1 are associated with crash risk; and Aim 2b: determine if those skill clusters from Aim1 are predictive of crashes in rural teen drivers. With a large sample of new driver applicants (>25,000), this study will examine, for the first time, differences in skill, starting at an age (adolescence) and a time (within the first year of licensure) when crash risk is high. We will create a normative dataset of novice driver skills; classify skill deficits by age, sex and time in learner period; and quantify the relationship between skill deficits and crash rates during the first 2 months of licensure (when crash rates are highest for young drivers) and the following 10 months as experience accumulates. If the use of the VDT proves successful in predicting crash risk, we will be able to identify novice drivers with elevated crash risk before granting a license. This R21 will enable the following lines of research: (1) Develop new interventions that address skill deficits through training, individualized to the needs of the novice driver (in partnership with Ohio). (2) Develop or adapt advanced driver assistance systems (ADAS) or self-driving vehicle technology to mitigate identified skill deficits (as part of Dr. Winston’s National Science Foundation Industry/University Cooperative Research Center). (3) Conduct a prospective study (R01) of the interacting trajectories of driving skill acquisition and cognitive development that lead to individual differences in skills at the time of licensure and the ensuing crash risk.

项目摘要 机动车碰撞导致青少年死亡，使其成为美国面临的主要健康威胁 青少年。鉴于95.6%的新手司机撞车是由于司机的错误，提高技能应该减少 碰撞发生率研究支持这一点：新手驾驶员撞车率在获得许可后立即达到峰值 并在获得许可后的几个月内随着经验的积累而急剧下降。但殊不知 驾驶执照上的哪些关键驾驶技能缺陷会导致撞车事故。这项研究的长期目标是 在获得执照之前制定干预措施，以确保获得执照后最安全的独立驾驶。 通过这个R21项目，我们将确定驾驶技能的缺陷，这些缺陷与以下主要预测因素有关： 早期碰撞风险（获得执照的年龄和性别），以及（B）可以预测第一次碰撞中碰撞率的差异 独立许可证年。我们将利用我们独家获得的创新，新的可用数据， 年轻司机的研究。为了能够研究新手驾驶员的安全关键驾驶技能缺陷， 结果从一个新的（截至2017年7月）虚拟驾驶测试（VDT）-由PI开发，通过国家 俄亥俄州，并在道路驾驶执照考试前立即交付-将与俄亥俄州的许可证挂钩 和碰撞数据。我们提出了这些目标：目标1：使用多变量聚类识别技能集群， 回归和机器学习技术来测试这些集群与年龄相关的假设， 驾照、性别和学习时间;目标2a：使用与报告的驾驶技能相关的数据 俄亥俄州的碰撞数据，以确定目标1中识别的技能集群是否与碰撞风险相关联; 目标2b：确定来自Aim 1的这些技能集群是否能预测农村青少年司机的撞车事故。与 新司机申请人的大样本（> 25，000），这项研究将首次研究， 技能，从一个年龄（青少年）和一个时间（在第一年的执照）时，碰撞风险高。 我们将创建一个新手驾驶技能的规范数据集;按年龄，性别和时间对技能缺陷进行分类， 学习者时期;并量化技能缺陷与前2个月撞车率之间的关系 的执照（当碰撞率最高的年轻司机）和以下10个月的经验 积累。如果VDT的使用被证明在预测撞车风险方面是成功的，我们将能够识别 新手驾驶员在获得许可证之前有较高的撞车风险。此R21将启用以下行 研究：（1）开发新的干预措施，通过培训解决技能缺陷， 新手司机的需求（与俄亥俄州合作）。(2)开发或调整高级驾驶员辅助系统 系统（ADAS）或自动驾驶汽车技术，以减轻识别的技能缺陷（作为博士的一部分）。 温斯顿国家科学基金会工业/大学合作研究中心）。(3)进行 驾驶技能获得和认知发展相互作用轨迹的前瞻性研究（R 01） 这导致了在获得执照时个人技能的差异以及随之而来的撞车风险。