Analysis and Processing of Driver Behavior for Emotion Recognition

Abstract

Road traffic injuries cause considerable economic losses to individuals, families and nations. Knowing the driver’s condition means continuously recognizing whether the driver is physically, emotionally and physiologically fit to drive the vehicle, as well as effectively communicating these situations to the driver. This research aims to collect, analyze and process behavioral signals in drivers through the interaction of the driver with the basic elements of driving to recognize different types of emotions established in the continuous model of emotional characterization proposed by Russell using emotion induction through augmented autobiographical recall and
machine learning algorithms, in order to generate models capable of recognizing the emotional state of drivers through a minimally invasive, objective and efficient process. With this methodology of signal analysis of driver behavior, 4 types of emotions could be recognized within the two-dimensional excitation-valence plane with an accuracy of 73% using the Random Forest algorithm. In  conclusion, a first scientific perspective on the relationship between driver behavior and emotions is offered, and the most significant information signal windows for emotion identification in a simulated driving experimentation environment are successfully identified.