01631461

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With rapid advancement in cellphone and in-vehicle technologies along with driver’s inclination to multitasking, the number of crashes due to distracted driving is a growing safety concern in our road network. Some previous studies attempted to detect distracted driving behavior in real-time to mitigate its adverse consequences. However, these studies mainly focused on detecting either visual or cognitive distractions only, while most of the real-life distracting tasks involve driver’s visual, cognitive, and physical workload, simultaneously. Additionally, previous studies frequently used eye, head, or face tracking data, though most current vehicles are not equipped with technologies to acquire such data. To address the above issues, this driving simulator study focused on developing algorithms for detecting specific distraction tasks that involve visual, cognitive, and physical workload using only vehicle control and driving performance measures. Specifically, algorithms were developed to detect driving behavior under two distracting tasks – texting and eating/drinking. Three data mining techniques were explored, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Support Vector Machine (SVM). SVM algorithms were found to outperform LDA and LR, which detected texting and eating/drinking distraction with an accuracy of 84.33% and 79.53%, respectively. The false alarm rates for these SVM algorithms were 15.77% and 23.54%, respectively. This study may provide useful guidance to successful implementation of distracted driver detection algorithm in Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications, as well as to auto manufacturers interested in integrating distraction detection systems in their vehicles.

This paper was sponsored by TRB committee AND30 Standing Committee on Simulation and Measurement of Vehicle and Operator Performance. Alternate title: Exploring Distracted Driver Detection Algorithms Using Driving Simulator Study.

01618707

17-05620

English

17p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Algorithms; Detection and identification; Distraction; Driving simulators; Highway safety

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-05620

TRIS, TRB, ATRI

Dec 8 2016 12:14PM