Driver distraction recognition based on smartphone sensor data

Jie Xie, Allaa R. Hilal, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)


Driver distraction is one of the leading causes of vehicle accidents and injury. Automated systems for identifying driver distraction are of great interest for improving road safety. This study develops a smartphone sensor based driver distraction system using an ensemble learning method. After data collection, linear velocity data is first linearly interpolated. Then, 3-axial acceleration and 3-axial gyro signals are filtered for reducing noise. Next, a sliding window is applied to IMU and GPS data collected by the smartphone for feature extraction, where temporal features are calculated. Ensemble learning of four standard classifiers is used to recognize distraction events: K-Nearest Neighbor, Logistic Regression, Gaussian Naive Bayes, Random Forest. To evaluate the proposed approach, 24 drivers were recruited to participate in a user study, driving on a route consisting of suburban and highway driving. Driver cognitive distraction was induced by asking the driver questions while driving. The experimental results show that the best weighted F1-score of our proposed system is 87% with all smartphone sensor signals.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
EditorsTadahiko Murata
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538666500
ISBN (Print)9781538666517
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Systems, Man and Cybernetics 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018 (Proceedings)


ConferenceIEEE International Conference on Systems, Man and Cybernetics 2018
Abbreviated titleSMC 2018
Internet address


  • Driver distraction classification
  • Ensemble learning
  • Smartphone

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