A Machine Learning Distracted Driving Prediction Model

Samira Ahangari, Mansoureh Jeihani, Abdollah Dehzangi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Distracted driving is known to be one of the core contributors to crashes in the U.S., accounting for about 40% of all crashes. Drivers' situational awareness, decision-making, and driving performance are impaired due to temporarily diverting their attention from the primary task of driving to other tasks not related to driving. Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator. In this study, we use a Bayesian Network classifier as a powerful machine learning algorithm on our trained data (80%) and tested (20%) with the data collected from a driving simulator, in which the 92 participants drove six scenarios of hand-held calling, hands-free calling, texting, voice command, clothing, and eating/drinking on four different road classes (rural collector, freeway, urban arterial, and local road in a school zone). Various driving performances such as speed, acceleration, throttle, lane changing, brake, collision, and offset from the lane center were investigated. Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. We also achieved 62.6% true positive rate, which demonstrates the ability of our model to correctly predict distractions.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450376259
DOIs
StatePublished - Aug 26 2019
Externally publishedYes
Event3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019 - Vancouver, Canada
Duration: Aug 26 2019Aug 28 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Vision, Image and Signal Processing, ICVISP 2019
CountryCanada
CityVancouver
Period8/26/198/28/19

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • Bayesian Network
  • Data Mining
  • Distracted Driving
  • Driving Simulator
  • Machine Learning

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