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Title: An ANN Model for Detecting Secondary Tasks from Driving Behavior Attributes: A Naturalistic Driving Study
Accession Number: 01630558
Record Type: Component
Abstract: Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the impact of different causes of distraction on driving behavior. However, only a few studies attempted to address how some driving behavior attributes could be linked to the cause of distraction. In essence, this study takes advantage of the rich Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) database to develop a model for detecting the likelihood of a driver’s involvement in secondary tasks from distinctive attributes of driving behavior. Five performance attributes, namely speed, longitudinal acceleration, lateral acceleration, yaw rate, and throttle position were used to describe the driving behavior. A model was developed for each of three selected secondary tasks: calling, texting, and passenger interaction. The models were developed using a supervised feed-forward Artificial Neural Network (ANN) architecture to account for the effect of inherent nonlinearity in the relationships between driving behavior and secondary tasks. The results show that the developed ANN models were able to detect the drivers’ involvement in calling, texting, and passenger interaction with an accuracy of 96.3%, 95.6%, and 95.2%, respectively. These results show that the selected driving performance attributes were effective in detecting the associated secondary tasks with driving behavior. The results are very promising and the developed models could potentially be applied in crash investigations to resolve legal disputes in traffic accidents.
Supplemental Notes: This paper was sponsored by TRB committee ADB10 Standing Committee on Traveler Behavior and Values.
Monograph Title: Monograph Accession #: 01618707
Report/Paper Numbers: 17-05057
Language: English
Corporate Authors: Transportation Research Board 500 Fifth Street, NW Authors: Ye, MengqiuOsman, Osama AIshak, SherifHashemi, BitaPagination: 15p
Publication Date: 2017
Conference:
Transportation Research Board 96th Annual Meeting
Location:
Washington DC, United States Media Type: Digital/other
Features: Figures; References
TRT Terms: Uncontrolled Terms: Subject Areas: Highways; Safety and Human Factors
Source Data: Transportation Research Board Annual Meeting 2017 Paper #17-05057
Files: TRIS, TRB, ATRI
Created Date: Dec 8 2016 11:57AM
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