Toward safer highways: predicting driver stress in varying conditions on habitual routes

Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is proposed to predict driver stress. This type of algorithm detects complex relationships among variables. At the same time, it avoids overfitting. The prediction of the upcoming stress level is made by taking into account driving behavior (acceleration, deceleration, speed) and the previous stress level.

prediction algorithms; stress measurement; heart rate variability; acceleration; vehicle safety; human factors

Toward safer highways: predicting driver stress in varying conditions on habitual routes Articles