Can we predict driver distraction without driver psychophysiological state?: a feasibility study on noninvasive distraction detection in manual driving

Abstract

Driver distraction is a major issue in manual driving, causing more than 30'000 fatal crashes on US roadways in 2015 only [11]. As such, it is widely studied in order to increase driving safety. Many studies show how to detect driver distraction using Machine Learning algorithms and driver psychophysiological data. In this study, we investigate the trade-off between efficiency and privacy while predicting driver distraction. Specifically, we want to assess the impact on the estimation of the driver state without access to his/her psychophysiological data. Different Machine Learning models (Convolutional Neural Networks, K-NN and Random forest) are implemented to evaluate the validity of the distraction detection with and without access to psychophysiological data. The results show that a Convolutional Neural Network model is still able to detect driver distraction without access to psychophysiological features, with an f1-score of 97.11%, losing only 1.37% in the process.