Mutual interferences of driving and texting performance

He, Jibo; Chaparro, Alex; Wu, Xiaohui; Crandall, Joseph M.; Ellis, Jake

doi:10.1016/j.chb.2015.05.004

Cited by 13 publications

(1 citation statement)

References 48 publications

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“…In our literature review, we observed that logistic regression is mainly used in studies whose main area is not computation. Works by Yannis et al [42], He et al [43] and Haque et al [41] applied logistic regression to investigate the driver distraction due to the use of the cell phones. In our study, we consider logistic regression as a baseline model.…”

Section: Machine-learning Modelsmentioning

confidence: 99%

A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving

Torres

Ohashi

Pessin

2019

Sensors

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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.

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Section: Machine-learning Modelsmentioning

confidence: 99%