Trust is important in determining how drivers interact with automated vehicles. Overtrust has contributed to fatal accidents; and distrust can hinder successful adoption of this technology. However, existing studies on trust are often hard to compare, given the complexity of the construct and the absence of standardized measures. Further, existing trust scales often do not consider its multi-dimensionality. Another challenge is that driving is strongly context- and situation-dependent. We present the Situational Trust Scale for Automated Driving, a short questionnaire to assess different aspects of situational trust, based on the trust model proposed by Hoff and Bashir. We evaluated the scale using an online study in the US and Germany (N=303), where participants faced different videos of an automated vehicle. Results confirm the existence of situational factors as components of trust, and support the scale being a valid measure of situational trust in this automated driving context.

Driving is a task that is often affected by emotions. The effect of emotions on driving has been extensively studied. Anger is an emotion that dominates in such investigations. Despite the knowledge on strong links between scents and emotions, few studies have explored the effect of olfactory stimulation in a context of driving. Such an outcome provides HCI practitioners very little knowledge on how to design for emotions using olfactory stimulation in the car. We carried out three studies to select scents of different valence and arousal levels (i.e. rose, peppermint, and civet) and anger eliciting stimuli (i.e. affective pictures and on-road events). We used this knowledge to conduct the fourth user study investigating how the selected scents change the emotional state, well-being, and driving behaviour of drivers in an induced angry state. Our findings enable better decisions on what scents to choose when designing interactions for angry drivers.

When evaluating drivers' trust in automated systems, perceived risk is an inevitable, yet underestimated component, especially during initial interaction. We designed two experimental studies focusing on how people assess risk in different driving environments and how introductory information about automation reliability influences trust and risk perception. First, we designed nine driving scenarios to determine which factors influence Perceived Situational Risk (PSR) and Perceived Relational Risk (PRR). Results showed that participants identified levels of risk based on traffic type and vehicles' abnormal behaviors. We then evaluated how introductory information and situational risk influence trust and PRR. Results showed that participants reported the highest level of trust, perceived automation reliability, and the lowest level of PRR when presented with information about a highly reliable system, and when driving in a low-risk situation. These results highlight the importance of incorporating perceived risk and introductory information to support the trust calibration in automated vehicles.