Lora Yekhshatyan

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OBJECTIVE In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. BACKGROUND Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash(More)
Driver distraction represents an increasingly important contributor to crashes and fatalities. Technology that can detect and mitigate distraction by alerting distracted drivers could play a central role in maintaining safety. Based on either eye measures or driver performance measures, numerous algorithms to detect distraction have been developed.(More)
Driver distraction contributes to approximately 43% of motor-vehicle crashes and 27% of near-crashes. Rapidly developing in-vehicle technology and electronic devices place additional demands on drivers, which might lead to distraction and diminished capacity to perform driving tasks. This situation threatens safe driving. Technology that can detect and(More)
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