Driving event recognition using machine learning and smartphones

Eilham Hakimie Bin Jamal Mohd Lokman, Vik Tor Goh*, Timothy Tzen Vun Yap, Hu Ng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The lack of real-time monitoring is one of the reasons for the lack of awareness among drivers of their dangerous driving behavior. This work aims to develop a driver profiling system where a smartphone’s built-in sensors are used alongside machine learning algorithms to classify different driving behaviors. Methods: We attempt to determine the optimal combination of smartphone sensors such as accelerometer, gyroscope, and GPS in order to develop an accurate machine learning algorithm capable of identifying different driving events (e.g. turning, accelerating, or braking). Results: In our preliminary studies, we encountered some difficulties in obtaining consistent driving events, which had the potential to add “noise” to the observations, thus reducing the accuracy of the classification. However, after some pre-processing, which included manual elimination of extraneous and erroneous events, and with the use of the Convolutional Neural Networks (CNN), we have been able to distinguish different driving events with an accuracy of about 95%. Conclusions: Based on the results of preliminary studies, we have determined that the proposed approach is effective in classifying different driving events, which in turn will allow us to determine driver’s driving behavior.

Original languageEnglish
Article number57
JournalF1000Research
Volume11
DOIs
Publication statusPublished - 19 Dec 2022

Keywords

  • convolutional neural networks
  • driver profiling
  • Machine learning
  • smartphone

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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