A first approach to understanding and measuring naturalness in driver-car interaction

With technology changing the nature of the driving task, qualitative methods can help designers understand and measure driver-car interaction naturalness. Fifteen drivers were interviewed at length in their own parked cars using ethnographically-inspired questions probing issues of interaction salience, expectation, feelings, desires and meanings. Thematic analysis and content analysis found five distinct components relating to 'rich physical' aspects of natural feeling interaction typified by richer physical, analogue, tactile styles of interaction and control. Further components relate to humanlike, intelligent, assistive, socially-aware 'perceived behaviours' of the car. The advantages and challenges of a naturalness-based approach are discussed and ten cognitive component constructs of driver-car naturalness are proposed. These may eventually be applied as a checklist in automotive interaction design.This research was fully funded by a research grant from Jaguar Land Rover, and partially funded by project n.220050/F11 granted by Research Council of Norway

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 driver’s 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 driver’s 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 user’s 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 driver’s 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 natürliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten Verständnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen über den emotionalen Zustand des Nutzers natürlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschäftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen. Dabei ermöglicht Emotionserkennung natürliche 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 Unfälle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern. Diese Dissertation beleuchtet Affective Automotive User Interfaces – zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug – auf Basis zweier inhaltlicher Säulen: erstens benutzen wir Ansätze zur Emotionsregulierung, um im Falle gefährlicher Fahrerzustände einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern. Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein großes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen Ansätze, die dazu dienen Fahrer in einer Gefühlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten Ansätze 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 beeinträchtigten Fahrern Unterstützung 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 großen 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 verfügbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoßen wir außerdem auf kulturelle und demografische Einflüsse, 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 zukünftige 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 driver’s 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 driver’s 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 user’s 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 driver’s 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 natürliche Interaktion, wie wir sie von Sprachassistenten kennen, profitiert von einem verbesserten Verständnis des Nutzerverhaltens. Zum Beispiel kann ein Assistent mit Informationen über den emotionalen Zustand des Nutzers natürlicher interagieren, vielleicht sogar Empathie zeigen. Affective Computing ist das damit verbundene Forschungsfeld, das sich damit beschäftigt menschliche Emotionen durch Beobachtung von physiologischen Daten, Sprache und Mimik zu erkennen. Dabei ermöglicht Emotionserkennung natürliche 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 Unfälle verringern und gleichzeitig Fahrer durch emotionale Interaktion begeistern. Diese Dissertation beleuchtet Affective Automotive User Interfaces – zu Deutsch in etwa Emotionsadaptive Benutzerschnittstellen im Fahrzeug – auf Basis zweier inhaltlicher Säulen: erstens benutzen wir Ansätze zur Emotionsregulierung, um im Falle gefährlicher Fahrerzustände einzugreifen. Zweitens erzeugen wir emotional aufgeladene Interaktionen, um das Nutzererlebnis zu verbessern. Erhöhte Sicherheit durch Emotionsregulierung: Emotionsadaptiven Systemen wird ein großes Potenzial zur Verbesserung der Verkehrssicherheit zugeschrieben. Wir stellen ein Modell und Methoden vor, die zur Untersuchung solcher Systeme benötigt werden und erforschen Ansätze, die dazu dienen Fahrer in einer Gefühlslage zu halten, die sicheres Handeln erlaubt. Die vorgestellten Methoden beinhalten Ansätze 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 beeinträchtigten Fahrern Unterstützung 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 großen 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 verfügbarer Verhaltensdaten ein emotional anspruchsvolles Erlebnis zu ermöglichen. In unserer Arbeit stoßen wir außerdem auf kulturelle und demografische Einflüsse, 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 zukünftige Systeme sicherheitsrelevantes Verhalten regulieren und gleichzeitig das Fortbestehen der Freude am Fahren ermöglichen

Designing for frustration and disputes in the family car

This article appears with the express permission of the publisher, IGI Global.Families spend an increasing amount of time in the car carrying out a number of activities including driving to work, caring for children and co-ordinating drop-offs and pickups. While families travelling in cars may face stress from difficult road conditions, they are also likely to be frustrated by coordinating a number of activities and resolving disputes within the confined space of car. A rising number of in-car infotainment and driver-assistance systems aim to help reduce the stress from outside the vehicle and improve the experience of driving but may fail to address sources of stress from within the car. From ethnographic studies of family car journeys, we examine the work of parents in managing multiple stresses while driving, along with the challenges of distractions from media use in the car. Keeping these family extracts as a focus for analysis, we draw out some design considerations that help build on the observations from our empirical work.Microsoft Research and the Dorothy Hodgkin Awar

Gesturing on the steering wheel, a comparison with speech and touch interaction modalities

This paper compares an emergent interaction modality for the In-Vehicle Infotainment System (IVIS), i.e., gesturing on the steering wheel, with two more popular modalities in modern cars: touch and speech. We conducted a betweensubjects experiment with 20 participants for each modality to assess the interaction performance with the IVIS and the impact on the driving performance. Moreover, we compared the three modalities in terms of usability, subjective workload and emotional response. The results showed no statically significant differences between the three interaction modalities regarding the various indicators for the driving task performance, while significant differences were found in measures of IVIS interaction performance: users performed less interactions to complete the secondary tasks with the speech modality, while, in average, a lower task completion time was registered with the touch modality. The three interfaces were comparable in all the subjective metrics

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

What If Your Car Would Care? Exploring Use Cases For Affective Automotive User Interfaces

In this paper we present use cases for affective user interfaces (UIs) in cars and how they are perceived by potential users in China and Germany. Emotion-aware interaction is enabled by the improvement of ubiquitous sensing methods and provides potential benefits for both traffic safety and personal well-being. To promote the adoption of affective interaction at an international scale, we developed 20 mobile in-car use cases through an inter-cultural design approach and evaluated them with 65 drivers in Germany and China. Our data shows perceived benefits in specific areas of pragmatic quality as well as cultural differences, especially for socially interactive use cases. We also discuss general implications for future affective automotive UI. Our results provide a perspective on cultural peculiarities and a concrete starting point for practitioners and researchers working on emotion-aware interfaces

My heart is racing! Psychophysiological dynamics of skilled racecar drivers

Our purpose was to test the multi-action plan (MAP) model assumptions in which athletes’ psychophysiological patterns differ among optimal and suboptimal performance experiences. Nine professional drivers competing in premier race categories (e.g., Formula 3, Porsche GT3 Cup Challenge) completed the study. Data collection involved monitoring the drivers’ perceived hedonic tone, accuracy on core components of action, posture, skin temperature, respiration rate, and heart rate responses during a 40-lap simulated race. Time marks, gathered at three standardized sectors, served as the performance variable. The A1GP racing simulator (Allinsport, Modena) established a realistic race platform. Specifically, the Barcelona track was chosen due to its inherently difficult nature characterized by intermittent deceleration points. Idiosyncratic analyses showed large individual differences in the drivers’ psychophysiological profile, as well as distinct patterns in regards to optimal and suboptimal performance experiences. Limitations and future research avenues are discussed. Action (e.g., attentional control) and emotion (e.g., biofeedback training) centered applied sport psychology implications are advanced

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars