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

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.

Managing Driving Modes in Automated Driving Systems

“…This is often accomplished via vehicle-oriented (e.g., acceleration or driving path) or driver-oriented (e.g., eye closure or hand position) approaches (Hecht et al 2018, Akai et al 2019. Given the substantial effect of driver behavior on roadway safety (Brookhuis andDe Waard 2010, Wang et al 2020), predictive models have been a focus of recent DSM research (Torres, Ohashi, and Pessin 2019;Yi et al 2019b), with a few notable examples adopting a Bayesian perspective (Agamennoni, Nieto, and Nebot 2011;Straub, Zheng, and Fisher 2014). We build upon this literature by constructing an alternative Bayesian model for DSM.…”

Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

“…The authors claimed to have results comparable to the most recent methods. Torres et al explored the machine learning algorithms to detect driver distraction due to smartphone usage [49]. The authors reported that more than 95% accuracy can be obtained using CNN and gradient boosting methods.…”

Decision support issues in automated driving systems

“…Whether human‐ or vehicle‐oriented, probabilistic and statistical‐based models have proven effective for the purpose of characterizing driver behavior. More specifically, machine‐learning models have been a primary focus of DSM research recently (Akai et al., 2019; Torres et al., 2019; Yi et al., 2019), with a few notable examples adopting a Bayesian perspective (Agamennoni et al., 2011; Straub et al., 2014). A critical determination in these statistical‐based models are the classification levels of a driver's state.…”