The goal of this study was to investigate changes in body temperature as indicators for the emotional dimension of power during driving. Therefore, a driving simulator experiment with 18 participants was conducted, in which two emotions (Fear and noFear) with different characteristics in the dimension of power (low power and high power), which is described as power or weakness feelings of control, were induced using threat and challenge events in the driving scenarios. Infrared thermography video and automatic facial feature recognition were implemented to assess participants' facial temperature. It was revealed that forehead temperature, which is supposed to represent emotional dimension of power, decreased significantly more after threat than after challenge events (t(17) = -1.83, p = .04, Cohen's d = 0.54). These results suggest that forehead temperature as an indicator for the emotional dimension of power can help to measure drivers' fear and thus aid reliable in-vehicle emotion recognition.
Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.
We are pleased to present to you the Adjunct Proceedings of the 11 th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '19), hosted by Utrecht University in the Netherlands. AutomotiveUI is the premier forum for user interface (UI) research in the automotive domain, bringing together researchers from both academia and industry. As in previous years, the papers and presentations of AutomotiveUI '19 target the development of novel vehicle technologies based on principled models and concepts that seek to enhance driver experience, safety, and performance; the development of semi and fully autonomous driving; and the needs of different user groups, including passengers and pedestrians. This year's topics covered a variety of themes. Specifically, the sessions focused on designing spaces and interfaces, Auto-UI today, driving assistance systems, automated driving, driver displays, multimodal interfaces, multimodal interaction, and visual-manual interaction. The papers are representative of the diverse disciplines and research methodologies that make key contributions to AutomotiveUI research, with submissions from both academia and industry. This year, we requested authors to specify the area of their research contribution, namely design and user experience, software and technology, user studies and human factors, research methods and simulation. We were pleased to have received submissions in all categories, which were suitably reviewed by experts in these areas. In particular, this year's submissions bears witness to a growth in the diversity of topics and institutes, relative to previous years. This highlights that our community is still growing and that AutomotiveUI research continues to be relevant across the world. Submission and Review Process The main papers track, which are reported in the main proceedings, feature papers of 6-10 pages. In total, we received 119 submissions: a record number for AutomotiveUI, with near 25% increase from previous year. The intensive review process was led by the technical program chairs, who were aided by 51 Associate Chairs, and 141 reviewers. Finally, we would like to thank our sponsors and exhibitors in supporting the conference. Sponsorship and exhibitors bring incomparable visibility to the leading conference on Automotive User Interfaces and we are grateful to have an exciting number of renowned international institutions and companies support us.
ZusammenfassungEin wichtiger Faktor für die Akzeptanz und damit die Verbreitung automatisierten und vernetzten Fahrens (AVF) ist der Grad der subjektiven Unsicherheit (Ungewissheit), den Nutzende bei der Interaktion mit automatisierten Fahrzeugen empfinden. Subjektive Unsicherheiten treten immer dann auf, wenn Personen aufgrund fehlender Erfahrung oder fehlender Informationen nicht in der Lage sind, den weiteren Verlauf einer Situation oder zukünftige Ereignisse vorherzusagen. Treten bei der Nutzung automatisierter Fahrzeuge solche Unsicherheiten auf, wird die Herausbildung von Vertrauen und damit von Akzeptanz für diese Technologie durch die Unsicherheit begleitende negative Emotionen beeinträchtigt. Im Rahmen des Projekts AutoAkzept (Automatisierung ohne Unsicherheit zur Erhöhung der Akzeptanz Automatisierten und Vernetzten Fahrens) wurden Lösungen für nutzerfokussierte Automatisierung entwickelt, die Fahrzeuginsassen in den Mittelpunkt der Systementwicklung stellen. Nutzerfokussierte Systeme berücksichtigen in der Mensch-Maschine-Interaktion zwei grundlegende menschliche Bedürfnisse, das Bedürfnis, zu verstehen (need to understand) und das Bedürfnis, verstanden zu werden (need to be understood). Dazu setzen nutzerfokussierte Systeme verschiedene Sensoren ein, um subjektive Unsicherheiten und ihre Einflussfaktoren in Echtzeit zu erkennen, diese Informationen mit Kontextdaten zu integrieren und Anpassungen vorzunehmen, die subjektive Unsicherheiten reduzieren. Die systemischen Anpassungen nutzerfokussierter Systeme folgen dabei einem ganzheitlichen Ansatz und berücksichtigen die Ebenen der Fahrzeugführung, der Interieuranpassung und Informationsdarbietung sowie der Zielführung. Durch die Reduzierung oder Vermeidung subjektiver Unsicherheiten unterstützen die Entwicklungen des Projekts eine positive, komfortable Benutzererfahrung und tragen zur Erhöhung der Akzeptanz von AVF bei. Die Arbeit präsentiert hierzu Forschungsergebnisse von AutoAkzept zu den Themen Zustands- und Aktivitätsmodellierung von Nutzenden sowie bedarfsgerechte Adaptionsstrategien, die einzelne Lösungsbausteine für die Umsetzung nutzerfokussierter Automation bilden.
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 (high novelty: Z = 2.80, p < 0.01, rcontrast = 0.46; low power: Z = 2.43, p < 0.05, rcontrast = 0.50). 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.
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