Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving

Automated driving has the potential to reduce the amount of fatal crashes, lighten the burden of commutes, and democratize mobility access to wider populations. But delegation of control to automation is not without issues. One of the foreseen drawbacks is that users might experience negative emotional reactions to unanticipated or unexplainable automated maneuvers. In this paper we present a novel method to induce targeted emotional reactions, frustration and startle, in simulated automated driving environments. We describe the data collection process for 17 driver - passenger dyads and discuss the data labelling method for generating reliable novel emotion datasets. This contribution is a foundational methodology towards expanding emotional understanding in automated vehicles, a critical skill for building long-term trusted experiences.