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AuthorArbabzadeh N.
AuthorJafari M.
AuthorJalayer M.
AuthorJiang S.
AuthorKharbeche M.
Available date2020-04-25T01:02:18Z
Publication Date2019
Publication NameTransportation Research Part C: Emerging Technologies
ResourceScopus
ISSN0968090X
URIhttp://dx.doi.org/10.1016/j.trc.2019.01.016
URIhttp://hdl.handle.net/10576/14406
AbstractThe National Transportation Safety Board (NTSB) estimates that 80% of the deaths and injuries resulting from rear-end collisions could be prevented by the use of advanced collision avoidance systems. While autonomous or higher-level vehicles will be equipped with this technology by default, most of the vehicles on our roadways will lack these advances, so rear-end crashes will dominate accident statistics for many years to come. However, a simple and cost-effective in-vehicle device that uses predictive tools and real-time driver-behavior and roadway data can significantly reduce the likelihood of these crashes. In this paper, we propose a hybrid physics/data-driven approach that can be used in a kinematic-based forward-collision warning system. In particular, we use a hierarchical regularized regression model to estimate driver reaction time based on individual driver characteristics, driving behavior, and surrounding driving conditions. This personalized reaction time is input into the Brill's one-dimensional car-following model to calculate the critical distance for collision warning. We use the Second Strategic Highway Research Program (SHRP-2)'s Naturalistic Driving Study (NDS) data, the largest and most comprehensive study of its kind, to model driver brake-to-stop response time. The results show that the inclusion of driver characteristics increases model precision in predicting driver reaction times.
SponsorThis publication was partially supported by a grant from the U.S. Department of Transportation , Office of the Secretary of Transportation (OST), Office of the Assistant Secretary for Research and Technology under Grant no. DTRT12-G-UTC16 and a grant from Qatar National Research Fund (QNRF) under Grant no. NPRP8-910-2-387 . The findings and conclusions of this study are those of the author and do not necessarily represent the views of the VTTI, SHRP 2, the Transportation Research Board, or the National Academies. Furthermore, the contents of this chapter reflect the views of the author, who is responsible for the facts and the accuracy of the information presented herein. The U.S. Government assumes no liability for the contents or use thereof.
Languageen
PublisherElsevier Ltd
SubjectForward collision warning system
Hierarchical regularized regression model
Reaction time estimation
SHRP-2 NDS data
TitleA hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data
TypeArticle
Pagination107-124
Volume Number100
dc.accessType Abstract Only


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