详情

Predicting Changes in Driving Safety Performance on an Individualized Level Under Naturalistic Driving Conditions

项目名称：	Predicting Changes in Driving Safety Performance on an Individualized Level Under Naturalistic Driving Conditions
摘要：	Transportation incidents remain a pressing public safety issue in the United States and throughout the world, despite significant advancements in vehicle safety technologies. The National Highway Traffic Safety Administration (NHTSA) estimates that about 20% of all crashes are fatigue-related, and as such has begun an initiative to reduce drowsy and distracted driving. Of particular interest are commercial truck drivers. In order to reduce the likelihood of incidents, it is important to understand the factors that affect driver safety performance in order to predict future changes in performance. The goal of this project is to examine how driver safety performance varies by location, time of day, hours on duty, and/or driver workload and to model the rate of change in performance to predict hazardous behaviors. To meet the overall goal, the following tasks will be completed: 1) model input parameters for characterizing workload: tasks performed, cognitive load, miles driven, road locations, driving characteristics; 2) quantify changes in driving performance based on mirror checks and system alerts and evaluate these changes with respect to gold standard guidelines; and 3) investigate data-driven modeling approaches for driving safety performance prediction, including structural analysis and machine learning approaches. This research makes use of data collected, through Maven Machines, during naturalistic conditions for a fleet of over 200 drivers and over 9 million driving events.
状态：	Active
资金：	75000
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	Office of the Assistant Secretary for Research and Technology
执行机构：	State University of New York, Buffalo
主要研究人员：	Cavuoto, Lora
开始时间：	20170901
预计完成日期：	20180831
实际结束时间：	0