The Effect of Vehicle Automation Styles on Drivers’ Emotional State

Abstract

Self-driving vehicles promise many safety, mobility, and environmental benefits. However, users’ lack of trust and acceptance may threaten the success and potential of this technology. Monitoring the driver’s emotional state is one way to address this challenge. Empathetic automation can respond to the driver’s state and improve the experience and acceptance of self-driving vehicle drivers. In this study, 24 participants rode in a self-driving vehicle simulator and experienced three automation styles (aggressive, moderate, conservative) and four intersection types (with and without a stop sign, and with and without traffic.) We identified the observed drivers’ emotions from the video data and labeled the video frames using the dimensional and discrete emotion models to examine how automation behavior affects the driver’s emotional state. We used multilevel Bayesian linear regression and multilevel Dirichlet regression to model the continuous and discrete emotions, respectively. The automation driving style effect varied for each participant. The same conditions provoked positive responses for some participants, and negative for others. Furthermore, the results showed that intersection type, the position within the intersection, and their interaction affected the driver’s emotional state. This indicates that personalized driver state monitoring systems might enhance drivers’ experience in self-driving vehicles.