Institute of Exact and Natural Sciences, Federal University of Pará (UFPA), Belém 66-075-110 PA, Brazil.
Informatics Department, Federal Institute of Pará, Vigia 68-780-000 PA, Brazil.
Sensors (Basel). 2019 Jul 19;19(14):3174. doi: 10.3390/s19143174.
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.
驾驶员分神是交通事故的主要原因之一。近年来，随着网络和社交网络的发展，驾驶时使用智能手机的情况变得更加频繁，成为安全的严重问题。在驾驶时发短信、打电话和阅读等行为都会造成驾驶员分神。在本文中，我们提出了一种非侵入性技术，仅使用智能手机传感器和机器学习数据，自动区分车内驾驶员和乘客阅读消息的行为。我们在不同场景下对七种前沿机器学习技术进行了建模和评估。在我们的实验中，卷积神经网络和梯度提升模型取得了最佳结果，准确率、精确率、召回率、F1 得分和kappa 指标均高于 0.95。
