Keeping an appropriate level of trust in automated driving (AD) is critical to safe driving. Although ample studies have investigated factors affecting trust in AD, few studies have investigated whether the personality of drivers influences the trust in AD system. Considering that trust measured at a given point in time lies on a continuum between dispositional and history-based trust, the current research investigated the relationship between driver’s personality and dispositional as well as history-based trust. We revealed that personality affected the two types of trust in different ways: A significant negative correlation emerged between Neuroticism and dispositional trust of AD (Study 1), whereas a significant negative correlation between Openness and history-based trust was found when participants interacted with the AD system (Study 2). These results suggest that drivers’ personality has an impact on the trust in AD, which is further modulated by the experience of driver’s interaction with the AD system.

Most gestural interaction studies on gesture elicitation have focused on hand gestures, and few have considered the involvement of other body parts. Moreover, most of the relevant studies used the frequency of the proposed gesture as the main index, and the participants were not familiar with the design space. In this study, we developed a gesture set that includes hand and non-hand gestures by combining the indices of gesture frequency, subjective ratings, and physiological risk ratings. We first collected candidate gestures in Experiment 1 through a user-defined method by requiring participants to perform gestures of their choice for 15 most commonly used commands, without any body part limitations. In Experiment 2, a new group of participants evaluated the representative gestures obtained in Experiment 1. We finally obtained a gesture set that included gestures made with the hands and other body parts. Three user characteristics were exhibited in this set: a preference for one-handed movements, a preference for gestures with social meaning, and a preference for dynamic gestures over static gestures.

Information technologies have been developed to facilitate driving performance and improve safety. However, there is a lack of computational methods that can take into account drivers’ adaptation to driving. That is, how behaviour changes with experience. Modelling the effect of driving experience on driver behaviour is important to the development of in-vehicle information technologies, because drivers at different skill levels may need different types or levels of assistance. Cognitive-architecture-based human performance modelling is a valuable method that can integrate different cognitive aspects underlying human behaviour such as skill levels and support quantitative simulation of behaviour. The study reported in this paper tested and examined computational models built in ACT-R (Adaptive Control of Thought-Rational) to account for the effect of driving experience on collision avoidance braking behaviour. The modelling results were compared with human data collected from a simulated driving experiment. The models produced braking behavioural results similar to the human results. Moreover, model predictions of three other emergent-braking scenarios were generally similar to and in the same order with the empirical results reported in previous studies. Future research can further integrate the method and results into intelligent driver assistance systems such as collision warning systems to better adjust the systems to the need of different drivers with different skill levels.