Citation

Torres R, Ohashi O, Pessin G. Sensors (Basel) 2019; 19(14): s19143174.

Affiliation

Robotics Lab, Instituto Tecnológico Vale, Ouro Preto 35-400-000 MG, Brazil. pessin@gmail.com.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19143174

PMID

31330929

Abstract

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments.

RESULTS show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

Language: en

Keywords

deep learning; driver distraction; machine learning; reading while driving