a machine-learning approach to distinguish passengers and drivers reading while driving

Clicks: 253
ID: 168033
2019
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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.
Reference Key
torres2019sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Renato Torres;Orlando Ohashi;Gustavo Pessin
Journal ekonomiczne problemy usług
Year 2019
DOI
10.3390/s19143174
URL
https://www.mdpi.com/1424-8220/19/14/3174
Keywords
Machine learning driver distraction

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