Frustration in traffic is one of the causes of aggressive driving. Knowledge whether a driver is frustrated may be utilized by future advanced driver assistance systems to counteract this source of crashes. One possibility to achieve this is to automatically recognize facial expressions of drivers. However, only little is known about the facial expressions of frustrated drivers. Here, we report the results of a driving simulator study investigating the facial muscle activity that comes along with frustration. Twenty-eight participants were video-taped (...) during frustrated and non-frustrated driving situations. Their facial muscle activity was manually coded according to the Facial Action Coding System. Participants showed significantly more facial muscle activity in the mouth region. Thus, recording facial muscle behavior potentially provides traffic researchers and assistance system developers with the possibility to recognize frustration while driving. (shrink)

Future automated vehicles of different sizes will share the same space with other road users, e. g., pedestrians. For a safe interaction, successful communication needs to be ensured, in particular, with vulnerable road users, such as pedestrians. Two possible communication means exist for AVs: vehicle kinematics for implicit communication and external human-machine interfaces for explicit communication. However, the exact interplay is not sufficiently studied yet for pedestrians' interactions with AVs. Additionally, very few other studies focused on the interplay of vehicle (...) kinematics and eHMI for pedestrians' interaction with differently sized AVs, although the precise coordination is decisive to support the communication with pedestrians. Therefore, this study focused on how the interplay of vehicle kinematics and eHMI affects pedestrians' willingness to cross, trust and perceived safety for the interaction with two differently sized AVs. In this experimental online study, the participants interacted with the AVs in a shared space. Both AVs were equipped with a 360° LED light-band eHMI attached to the outer vehicle body. Three eHMI statuses were displayed. The vehicle kinematics were varied at two levels. Moreover, “non-matching” conditions were included for both AVs in which the dynamic eHMI falsely communicated a yielding intent although the vehicle did not yield. Overall, results showed that pedestrians' willingness to cross was significantly higher for the smaller AV compared to the larger AV. Regarding the interplay of vehicle kinematics and eHMI, results indicated that a dynamic eHMI increased pedestrians' perceived safety when the vehicle yielded. When the vehicle did not yield, pedestrians' perceived safety still increased for the dynamic eHMI compared to the static eHMI and no eHMI. The findings of this study demonstrated possible negative effects of eHMIs when they did not match the vehicle kinematics. Further implications for a holistic communication strategy for differently sized AVs will be discussed. (shrink)

Facial expressions are one of the commonly used implicit measurements for the in-vehicle affective computing. However, the time courses and the underlying mechanism of facial expressions so far have been barely focused on. According to the Component Process Model of emotions, facial expressions are the result of an individual's appraisals, which are supposed to happen in sequence. Therefore, a multidimensional and dynamic analysis of drivers' fear by using facial expression data could profit from a consideration of these appraisals. A driving (...) simulator experiment with 37 participants was conducted, in which fear and relaxation were induced. It was found that the facial expression indicators of high novelty and low power appraisals were significantly activated after a fear event. Furthermore, after the fear event, the activation of high novelty occurred earlier than low power. These results suggest that multidimensional analysis of facial expression is suitable as an approach for the in-vehicle measurement of the drivers' emotions. Furthermore, a dynamic analysis of drivers' facial expressions considering of effects of appraisal components can add valuable information for the in-vehicle assessment of emotions. (shrink)