1,081 research outputs found
Affective automotive user interfaces
Technological progress in the fields of ubiquitous sensing and machine learning has been fueling the development of user-aware human-computer interaction in recent years. Especially natural user interfaces, like digital voice assistants, can benefit from understanding their users in order to provide a more naturalistic experience. Such systems can, for example, detect the emotional state of users and accordingly act in an empathic way. One major research field working on this topic is Affective Computing, where psycho-physiological measures, speech input, and facial expressions are used to sense human emotions.
Affective data allows natural user interfaces to respond to emotions, providing promising perspectives not only for user experience design but also for safety aspects. In automotive environments, informed estimations of the driverâs state can potentially avoid dangerous errors and evoking positive emotions can improve the experience of driving.
This dissertation explores Affective Automotive User Interfaces using two basic interaction paradigms: firstly, emotion regulation systems react to the current emotional state of the user based on live sensing data, allowing for quick interventions. Secondly, emotional interaction synthesizes experiences which resonate with the user on an emotional level. The constituted goals of these two interaction approaches are the promotion of safe behavior and an improvement of user experience.
Promoting safe behavior through emotion regulation: Systems which detect and react to the driverâs state are expected to have great potential for improving road safety. This work presents a model and methods needed to investigate such systems and an exploration of several approaches to keep the driver in a safe state. The presented methods include techniques to induce emotions and to sample the emotional state of drivers. Three driving simulator studies investigate the impacts of emotionaware interventions in the form of implicit cues, visual mirroring and empathic speech synthesis. We envision emotion-awareness as a safety feature which can detect if a driver is unfit or in need of support, based on the propagation of robust emotion detection technology.
Improving user experience with emotional interaction: Emotional perception is an essential part of user experience. This thesis entails methods to build emotional experiences derived from a variety of lab and simulator studies, expert feedback, car-storming sessions and design thinking workshops. Systems capable of adapting to the userâs preferences and traits in order to create an emotionally satisfactory user experience do not require the input of emotion detection. They rather create value through general knowledge about the user by adapting the output they generate. During this research, cultural and generational influences became evident, which have to be considered when implementing affective automotive user interfaces in future cars.
We argue that the future of user-aware interaction lies in adapting not only to the driverâs preferences and settings but also to their current state. This paves the way for the regulation of safe behavior, especially in safety-critical environments like cars, and an improvement of the driving experience.Aktuelle Fortschritte in den Bereichen des Machine Learning und Ubiquitous Computing ermöglichen es heute adaptive Mensch-Maschine-Schnittstellen zu realisieren. Vor allem natuÌrliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten VerstĂ€ndnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen uÌber den emotionalen Zustand des Nutzers natuÌrlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschĂ€ftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen.
Dabei ermöglicht Emotionserkennung natuÌrliche Interaktion auf Basis des Fahrer/innenzustands, was nicht nur vielversprechend in Bezug auf die Gestaltung des Nutzerelebnisses klingt, sondern auch Anwendungen im Bereich der Verkehrssicherheit hat. Ein Einsatz im Fahrkontext könnte so vermeidbare UnfĂ€lle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern.
Diese Dissertation beleuchtet Affective Automotive User Interfaces â zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug â auf Basis zweier inhaltlicher SĂ€ulen: erstens benutzen wir AnsĂ€tze zur Emotionsregulierung, um im Falle gefĂ€hrlicher FahrerzustĂ€nde einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern.
Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein groĂes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen AnsĂ€tze, die dazu dienen Fahrer in einer GefuÌhlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten AnsĂ€tze zur Emotionsinduktion und -erkennung, sowie drei Fahrsimulatorstudien zur Beeinflussung von Fahrern durch indirekte Reize, Spiegeln von Emotionen und empathischer Sprachinteraktion. Emotionsadaptive Sicherheitssysteme können in Zukunft beeintrĂ€chtigten Fahrern UnterstuÌtzung leisten und so den Verkehr sicherer machen, vorausgesetzt die technischen Grundlagen der Emotionserkennung gewinnen an Reife.
Verbesserung des Nutzererlebnisses durch emotionale Interaktion: Emotionen tragen einen groĂen Teil zum Nutzerlebnis bei, darum ist es nur sinnvoll den zweiten Fokuspunkt dieser Arbeit auf systeminitiierte emotionale Interaktion zu legen.Wir stellen die Ergebnisse nutzerzentrierter Ideenfindung und mehrer Evaluationsstudien der resultierenden Systeme vor. Um sich den Vorlieben und Eigenschaften von Nutzern anzupassen wird nicht zwingend Emotionserkennung benötigt. Der Mehrwert solcher Systeme besteht vielmehr darin, auf Basis verfuÌgbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoĂen wir auĂerdem auf kulturelle und demografische EinfluÌsse, die es bei der Gestaltung von emotionsadaptiven Nutzerschnittstellen zu beachten gibt.
Wir sehen die Zukunft nutzeradaptiver Interaktion im Fahrzeug nicht in einer rein verhaltensbasierten Anpassung, sondern erwarten ebenso emotionsbezogene Innovationen. Dadurch können zukuÌnftige Systeme sicherheitsrelevantes Verhalten regulieren und gleichzeitig das Fortbestehen der Freude am Fahren ermöglichen
Affective automotive user interfaces
Technological progress in the fields of ubiquitous sensing and machine learning has been fueling the development of user-aware human-computer interaction in recent years. Especially natural user interfaces, like digital voice assistants, can benefit from understanding their users in order to provide a more naturalistic experience. Such systems can, for example, detect the emotional state of users and accordingly act in an empathic way. One major research field working on this topic is Affective Computing, where psycho-physiological measures, speech input, and facial expressions are used to sense human emotions.
Affective data allows natural user interfaces to respond to emotions, providing promising perspectives not only for user experience design but also for safety aspects. In automotive environments, informed estimations of the driverâs state can potentially avoid dangerous errors and evoking positive emotions can improve the experience of driving.
This dissertation explores Affective Automotive User Interfaces using two basic interaction paradigms: firstly, emotion regulation systems react to the current emotional state of the user based on live sensing data, allowing for quick interventions. Secondly, emotional interaction synthesizes experiences which resonate with the user on an emotional level. The constituted goals of these two interaction approaches are the promotion of safe behavior and an improvement of user experience.
Promoting safe behavior through emotion regulation: Systems which detect and react to the driverâs state are expected to have great potential for improving road safety. This work presents a model and methods needed to investigate such systems and an exploration of several approaches to keep the driver in a safe state. The presented methods include techniques to induce emotions and to sample the emotional state of drivers. Three driving simulator studies investigate the impacts of emotionaware interventions in the form of implicit cues, visual mirroring and empathic speech synthesis. We envision emotion-awareness as a safety feature which can detect if a driver is unfit or in need of support, based on the propagation of robust emotion detection technology.
Improving user experience with emotional interaction: Emotional perception is an essential part of user experience. This thesis entails methods to build emotional experiences derived from a variety of lab and simulator studies, expert feedback, car-storming sessions and design thinking workshops. Systems capable of adapting to the userâs preferences and traits in order to create an emotionally satisfactory user experience do not require the input of emotion detection. They rather create value through general knowledge about the user by adapting the output they generate. During this research, cultural and generational influences became evident, which have to be considered when implementing affective automotive user interfaces in future cars.
We argue that the future of user-aware interaction lies in adapting not only to the driverâs preferences and settings but also to their current state. This paves the way for the regulation of safe behavior, especially in safety-critical environments like cars, and an improvement of the driving experience.Aktuelle Fortschritte in den Bereichen des Machine Learning und Ubiquitous Computing ermöglichen es heute adaptive Mensch-Maschine-Schnittstellen zu realisieren. Vor allem natuÌrliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten VerstĂ€ndnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen uÌber den emotionalen Zustand des Nutzers natuÌrlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschĂ€ftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen.
Dabei ermöglicht Emotionserkennung natuÌrliche Interaktion auf Basis des Fahrer/innenzustands, was nicht nur vielversprechend in Bezug auf die Gestaltung des Nutzerelebnisses klingt, sondern auch Anwendungen im Bereich der Verkehrssicherheit hat. Ein Einsatz im Fahrkontext könnte so vermeidbare UnfĂ€lle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern.
Diese Dissertation beleuchtet Affective Automotive User Interfaces â zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug â auf Basis zweier inhaltlicher SĂ€ulen: erstens benutzen wir AnsĂ€tze zur Emotionsregulierung, um im Falle gefĂ€hrlicher FahrerzustĂ€nde einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern.
Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein groĂes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen AnsĂ€tze, die dazu dienen Fahrer in einer GefuÌhlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten AnsĂ€tze zur Emotionsinduktion und -erkennung, sowie drei Fahrsimulatorstudien zur Beeinflussung von Fahrern durch indirekte Reize, Spiegeln von Emotionen und empathischer Sprachinteraktion. Emotionsadaptive Sicherheitssysteme können in Zukunft beeintrĂ€chtigten Fahrern UnterstuÌtzung leisten und so den Verkehr sicherer machen, vorausgesetzt die technischen Grundlagen der Emotionserkennung gewinnen an Reife.
Verbesserung des Nutzererlebnisses durch emotionale Interaktion: Emotionen tragen einen groĂen Teil zum Nutzerlebnis bei, darum ist es nur sinnvoll den zweiten Fokuspunkt dieser Arbeit auf systeminitiierte emotionale Interaktion zu legen.Wir stellen die Ergebnisse nutzerzentrierter Ideenfindung und mehrer Evaluationsstudien der resultierenden Systeme vor. Um sich den Vorlieben und Eigenschaften von Nutzern anzupassen wird nicht zwingend Emotionserkennung benötigt. Der Mehrwert solcher Systeme besteht vielmehr darin, auf Basis verfuÌgbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoĂen wir auĂerdem auf kulturelle und demografische EinfluÌsse, die es bei der Gestaltung von emotionsadaptiven Nutzerschnittstellen zu beachten gibt.
Wir sehen die Zukunft nutzeradaptiver Interaktion im Fahrzeug nicht in einer rein verhaltensbasierten Anpassung, sondern erwarten ebenso emotionsbezogene Innovationen. Dadurch können zukuÌnftige Systeme sicherheitsrelevantes Verhalten regulieren und gleichzeitig das Fortbestehen der Freude am Fahren ermöglichen
On driver behavior recognition for increased safety:A roadmap
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account driversâ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative HumanâMachine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced
Affective Role of the Future Autonomous Vehicle Interior
Recent advancements in autonomous technology allow for new opportunities in
vehicle interior design. Such a shift in in-vehicle activity suggests vehicle
interior spaces should provide an adequate manner by considering users'
affective desires. Therefore, this study aims to investigate the affective role
of future vehicle interiors. Thirty one participants in ten focus groups were
interviewed about challenges they face regarding their current vehicle interior
and expectations they have for future vehicles. Results from content analyses
revealed the affective role of future vehicle interiors. Advanced exclusiveness
and advanced convenience were two primary aspects identified. The identified
affective roles of each aspect are a total of eight visceral levels, four
visceral levels each, including focused, stimulating, amused, pleasant, safe,
comfortable, accommodated, and organized. We expect the results from this study
to lead to the development of affective vehicle interiors by providing the
fundamental knowledge for developing conceptual direction and evaluating its
impact on user experiences.Comment: 15 pages, 4 figures, 2 table
Affective driver-pedestrian interaction: Exploring driver affective responses toward pedestrian crossing actions using camera and physiological sensors
Eliciting and capturing drivers' affective responses in a realistic outdoor setting with pedestrians poses a challenge when designing in-vehicle, empathic interfaces. To address this, we designed a controlled, outdoor car driving circuit where drivers (N=27) drove and encountered pedestrian confederates who performed non-verbal positive or non-positive road crossing actions towards them. Our findings reveal that drivers reported higher valence upon observing positive, non-verbal crossing actions, and higher arousal upon observing non-positive crossing actions. Drivers' heart signals (BVP, IBI and BPM), skin conductance and facial expressions (brow lowering, eyelid tightening, nose wrinkling, and lip stretching) all varied significantly when observing positive and non-positive actions. Our car driving study, by drawing on realistic driving conditions, further contributes to the development of in-vehicle empathic interfaces that leverage behavioural and physiological sensing. Through automatic inference of driver affect resulting from pedestrian actions, our work can enable novel empathic interfaces for supporting driver emotion self-regulation
Exploring emotion responses toward pedestrian crossing actions for designing in-vehicle empathic interfaces
While affective non-verbal communication between pedestrians and drivers has been shown to improve on-road safety and driving experiences, it remains a challenge to design driver assistance systems that can automatically capture these affective cues. In this early work, we identify users' emotional self-report responses towards commonly occurring pedestrian actions while crossing a road. We conducted a crowd-sourced web-based survey (N=91), where respondents with prior driving experience viewed videos of 25 pedestrian interaction scenarios selected from the JAAD (Joint Attention for Autonomous Driving) dataset, and thereafter provided valence and arousal self-reports. We found participants' emotion self-reports (especially valence) are strongly influenced by actions including hand waving, nodding, impolite hand gestures, and inattentive pedestrian(s) crossing while engaged with a phone. Our findings provide a first step towards designing in-vehicle empathic interfaces that can assist in driver emotion regulation during on-road interactions, where the identified pedestrian actions serve as future driver emotion induction stimuli
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Affective scenarios in automotive design: a human-centred approach towards understanding of emotional experience
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing a period of significant transformation due to the arrival of many
new digital technologies. As the focus of automotive engineering has shifted from hardware to
software, the conventional processes of making, buying and owning an automobile have changed.
Peoplesâ desires for new automotive experiences are increasing; they demand more sophisticated
approaches to the automotive experience beyond merely improving functional requirements for
advanced automation systems, interfaces and connectivity. Thus, it is essential to understand
human experience in order to help people deal with the high degree of complexity in the driving
environment and to help them to cope with unanticipated driving events that involve emotional,
psychological or sociological issues.
This research takes a human-centred approach to investigating real-life scenarios in which people
emotionally engage with automobiles with the aim of developing a relevant set of scenarios for
this context. An extensive literature review was conducted of human emotion, memory systems,
emotional memory characteristics, scenarios, and scenarios with emotional aspects, followed by a
discussion defining scenario development process and affective scenarios.
This research provides a methodology for in-depth qualitative studies that develop affective design
scenarios with automobiles. As a triangulation approach, two independent studies in different
settings explored affective scenario themes in automotive contexts of peopleâs real-life car stories
that made them respond emotionally. The themes that were revealed from both studies were
consolidated, and exemplary scenarios of 13 consolidated main themes were formulated to
illustrate a set of affective scenarios in automotive contexts. This research leads to an enhanced
understanding of a set of critical contexts that automotive practitioners should take into account
for future automotive design. Suggestions with possible questions based on the research outcome
provide opportunities for them to agilely cope with unanticipated future events, whereby highly
complex driving environment by connected and autonomous vehicles. This methodology used here
can be replicated for future affective scenario studies focusing on specific products, sub-systems
or services such as navigation systems or car-sharing services. The results, which have been
validated through a triangulation approach, can bolster the automobile design process by
addressing potential issues and challenges in automotive experience by facilitating idea generation,
enhancing a shared understanding of critical contexts and by assisting decision-making among
stakeholders from different departments
From video to hybrid simulator: Exploring affective responses toward non-verbal pedestrian crossing actions using camera and physiological sensors
Capturing driversâ affective responses given driving context and driver-pedestrian interactions remains a challenge for designing in-vehicle, empathic interfaces. To address this, we conducted two lab-based studies using camera and physiological sensors. Our first study collected participantsâ (N = 21) emotion self-reports and physiological signals (including facial temperatures) toward non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving dataset. Our second study increased realism by employing a hybrid driving simulator setup to capture participantsâ affective responses (N = 24) toward enacted, non-verbal pedestrian crossing actions. Key findings showed: (a) non-positive actions in videos elicited higher arousal ratings, whereas different in-video pedestrian crossing actions significantly influenced participantsâ physiological signals. (b) Non-verbal pedestrian interactions in the hybrid simulator setup significantly influenced participantsâ facial expressions, but not their physiological signals. We contribute to the development of in-vehicle empathic interfaces that draw on behavioral and physiological sensing to in-situ infer driver affective responses during non-verbal pedestrian interactions
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Automotive emotions: a human-centred approach towards the measurement and understanding of drivers' emotions and their triggers
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing significant technological and sociological shifts, calling for an improved understanding of driver and passenger behaviours, emotions and needs, and a transformation of the traditional
automotive design process. This research takes a human-centred approach to automotive research, investigating the usersâ emotional states during automobile driving, with the goal to develop a framework for automotive emotion research, thus enabling the integration of technological advances into the driving environment. A literature review of human emotion and emotion in an automotive context was conducted, followed by three driving studies investigating emotion through Facial-Expression Analysis (FEA): An exploratory study investigated whether emotion elicitation can be applied in driving simulators, and if FEA can detect the emotions triggered. The results allowed confidence in the applicability of emotion elicitation to a lab-based environment to trigger emotional responses, and FEA to detect those. An on-road driving study was conducted in a natural setting to investigate whether natures and frequencies of emotion events could be automatically measured. The possibility of assigning triggers to those was investigated. Overall, 730 emotion events were detected during a total driving time of 440 minutes, and event triggers were assigned to 92% of the emotion events. A similar second on-road study was conducted in a partially controlled setting on a planned road circuit. In 840 minutes, 1947 emotion events were measured, and triggers were successfully assigned to 94% of those. The differences in natures, frequencies and causes of emotions on different road
types were investigated. Comparison of emotion events for different roads demonstrated substantial variances of natures, frequencies and triggers of emotions on different road types. The results showed that emotions play a significant role during automobile driving. The possibility of assigning triggers can be used to create a better understanding of causes of emotions in the automotive habitat. Both on-road studies were compared through statistical analysis to investigate influences of the different study settings. Certain conditions (e.g.
driving setting, social interaction) showed significant influence on emotions during driving. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified. The methodology and results can be applied to design and research processes, allowing the identification of issues and opportunities in current automotive design to address challenges of future automotive design. Suggested future research includes the investigation of a wider variety of road types and situations, testing with different automobiles and the combination of multiple measurement techniques
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