Deep Learning-based Emotion Recognition for Human-Vehicle Interaction in Autonomous Vehicles

Abstract

The study of emotions plays an important role in scenarios where the interaction with human beings is involved. By studying human emotions, it becomes possible to make predictions about decision-making related to driving. Based on previous studies, which were based on the use of abstract artifacts in interaction, such as visual maps for navigation, it is worth considering the use of artificial intelligence based on facial and vocal expression for the analysis of drivers' performance and affect. Regulating interaction on driver behavior through synthetic companions (or wholly automated agents) could enhance driving pleasure and assist in the prevention of accidents. One of the key tasks linked to reinforcement of machine understanding of emotions is emotion recognition. Learned representations can help build an emotional model of the vehicle environment. In this paper, we present our solution specifically designed to make emotional detection in autonomous vehicle, to stimulate this aspect of human-vehicle interaction. We made this system a focus of interest for studying three main vehicles, passenger seats.[2] In the domain of human-robot interaction (HRI), the principle function of these models is to make persons and robots understand one another well enough to work together as a unit with a purpose. Human driver’s temper is the major issue in automobile security.