###### Abstract  
The adoption of self-driving technologies requires addressing public concerns about their reliability and trustworthiness. To understand how user experience in self-driving vehicles is influenced by the level of risk and head-up display (HUD) information, using virtual reality (VR) and a motion simulator, we simulated risky situations including accidents with HUD information provided under different conditions. The findings revealed how HUD information related to the immediate environment and the accident's severity influenced the user experience (UX). Further, we investigated galvanic skin response (GSR) and self-reported emotion (Valence and Arousal) annotation data and analyzed correlations between them. The results indicate significant differences and correlations between GSR data and self-reported annotation data depending on the level of risk and whether or not information was provisioned through HUD. Hence, VR simulations combined with motion platforms can be used to observe the UX (trust, perceived safety, situation awareness, immersion and presence, and reaction to events) of self-driving vehicles while controlling the road conditions such as risky situations. Our results indicate that HUD information provision significantly increases trust and situation awareness of the users, thus improving the user experience in self-driving vehicles. autonomous vehicles; driving simulator; human-machine interaction; physiological signal; reliability; user experience; virtual reality +
Footnote †: journal: Medical  
In this study, we chose the level of risk and that of the road information provision on HUDs as two dimensions, based on previous studies [9; 10]. [PERSON] et al. showed that providing evidence that supports self-driving vehicles' decisions is important for cultivating trust [10]. [PERSON] et al. observed changes in trust according to the degree of risk. Although these factors affect trust in self-driving vehicles, only few studies have considered both factors in a single scenario. Based on these insights, we developed realistic driving content in virtual reality (VR) that simulates traffic signals. This allowed us to expose subjects to a range of risky driving scenarios, including simulated car accidents, and track their reactions and perceptions of self-driving vehicles under different conditions. The contributions of this study are as follows: (1) We developed a realistic traffic VR system that provides an immersive self-driving vehicle experience with synchronized physical movement to the users. The developed system enables risky simulations which are impossible to be conducted in the real world, and enables complete control of road conditions to test the same scenario repeatedly. (2) We observed the UX change as the level of the information provided and the risk changed using the self-driving VR system. (3) We observed that the physiological and self-reported annotation data significantly change as the level of the information provided and the risk changes using the self-driving VR system. Eight scenarios were developed to observe the effects of two factors(level of risk, and level of information provision). We conducted experiments on 52 subjects and analyzed the quantitative and qualitative UX results to provide insights into designing self-driving vehicle experiences. To assess trust in self-driving vehicles under these conditions, we considered both quantitative and qualitative metrics. We collected self-reported questionnaire data and galvanic skin responses (GSR). By comparing quantitative and qualitative data, we ensured the use of self-reported measures of valence and arousal. Valence and arousal(VA) are two commonly used dimensions of human emotion, and we validated whether our self-driving simulation provides an appropriate sense of emotion to users by observing VA annotation values.