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Resurrecting the ghost in the shell: A need-centered development approach for optimizing user experience in highly automated vehicles
... Studies investigating these ironies of automation have clearly demonstrated that "the more advanced a control system is, the more crucial the contribution of the human operator" (Bainbridge, 1983, p. 775). In other words, the higher the reliability of an automated system, the more substantial the Human Factors challenges become (Bainbridge, 1983;Parasuraman and Riley, 1997;Kyriakidis et al., 2017;Boelhouwer et al., 2019;Carsten and Martens, 2019;Frison et al., 2019;Walker, 2021). ...
There is a growing body of research on trust in driving automation systems. In this paper, we seek to clarify the way trust is conceptualized, calibrated and measured taking into account issues related to specific levels of driving automation. We find that: 1) experience plays a vital role in trust calibration; 2) experience should be measured not just in terms of distance travelled, but in terms of the range of situations encountered; 3) system malfunctions and recovery from such malfunctions is a fundamental part of this experience. We summarize our findings in a framework describing the dynamics of trust calibration. We observe that methods used to quantify trust often lack objectivity, reliability, and validity, and propose a set of recommendations for researchers seeking to select suitable trust measures for their studies. In conclusion, we argue that the safe deployment of current and future automated vehicles depends on drivers developing appropriate levels of trust. Given the potentially severe consequences of miscalibrated trust, it is essential that drivers incorporate the possibility of new and unexpected driving situations in their mental models of system capabilities. It is vitally important that we develop methods that contribute to this goal.
... There is also growing importance for highly automated systems to be designed and evaluated under a holistic lens. Frison, Wintersberger, and Riener's AD UX framework (Frison et al., 2019) utilizes a variety of evaluation methods and measures to fuse the values of differing UX design perspectives (experience-oriented, product-oriented, and behavioral-orientated). This model emphasizes how technical restrictions, the operational design domain (ODD), and level of automation can provide unique challenges that shape UX quality. ...
Advanced driver assistance systems (ADAS) have been increasingly incorporated in cars for nearly four decades and have changed the relationship of the driver to the driving task substantially. Over this period, original equipment manufacturers (OEMs) have developed similar ADAS functions (e.g., adaptive cruise control, lane keeping assist, forward collision warning), but these functions lack uniformity in their implementation such that there is the possibility of negative transfer of learning across different implementations of the same ADAS function. This brief theoretical paper aims to highlight issues around using some existing human-automation interaction (HAI) frameworks that have been used to classify vehicle automation and discuss considerations for better ADAS classification to inform their design and support safe and satisfactory use.
... The levels of vehicle automation have revealed a great variety of issues in driver-vehicle interaction (e.g., takeover control [12], situation awareness [2], or trust [5]), which need to be resolved as long as drivers are included in the control loop [10,11,13]. Even when drivers are no longer required to perform driving tasks partly, human needs must be considered in vehicle design -for example, regarding desired driving styles [1,3]. However, this is a more crucial issue than just deciding if a vehicle should drive more aggressively or passively in certain situations [6]. ...
... In line, not only the motivational regulations can be examined further, but also the basic psychological needs autonomy, competence, and relatedness as proposed by the SDT can be addressed in future studies and practical applications. Only recently, first applications on needs in autonomous driving have been proposed (Detjen et al., 2021;Frison et al., 2019), so this should be further elaborated in the context of motivation and acceptance. Especially from a practical perspective, as the car is discussed as future living space and technology in daily lives is increasing, it is crucial to explore how user needs and motivations are associated with the acceptance, user experience, and overall well-being. ...
More and more technical systems enter the vehicle impacting drivers’ experiences. In the human-centered design, an understanding of influencing factors for acceptance and usage is crucial to align in-vehicle technology with the user needs. Addressing the underlying psychological processes, this work modelled drivers’ usage intentions with motivational regulations (SDT), the TAM, and the UTAUT. An online study with 319 German drivers was conducted examining drivers’ positive or negative experiences with assistance and infotainment systems in the vehicle. In linear regressions, the TAM and UTAUT predicted the acceptance equally for assistance and navigation systems. Amotivation, identified regulation, and intrinsic regulation enhanced the prediction of usage intentions by 3.0–15.4% in addition to the UTAUT variables revealing the additional benefit of incorporating the motivational perspective into the modeling of in-vehicle technology acceptance. Future research and practitioners can build upon this theoretical basis and recommendations on improving motivation and well-being.
... " to indicate the performance expectancy of autonomous public transportation. Second, we analyzed research on user needs during automated driving [3] such as the need for competence, autonomy, and stimulation. For instance, for the need for autonomy, we created a statement that Performance Expectancy I think driverless public transportation would be useful. ...
Public transportation will become highly automated in the future, and at some point, human drivers are no longer necessary. Today many people are skeptical about such scenarios of autonomous public transport (abbr.: APT). In this paper, we assess users’ subjective priority of different factors that lead to personal acceptance or rejection of APT using an adapted online version of the Q-Methodology with 44 participants. We found four prototypical attitudes to which subgroups of participants relate: 1) technical enthusiasts, 2) social skeptics, 3) service-oriented non-enthusiasts, and 4) technology-oriented non-enthusiasts. We provide an unconventional perspective on APT acceptance that helps practitioners prioritize design requirements and communicate, targeting users’ specific attitudes.
... Third, we covered limited aspects of user experience (i.e., positive/negative emotional experience) and subjective usability. Future studies can probe these topics following a formal framework or model of user experience (Eckoldt et al., 2013;Hassenzahl, 2018;Frison et al., 2019) and cover more aspects of user experience with interactive technologies (e.g., hedonic attributes). Fourth, our results were constrained by our L3 AV and ADS and thus cannot be interpreted as formal evaluations of current AVs. ...
We are entering an era of automated vehicles (AVs), which has potential to improve road safety considerably. A compelling user experience is crucial to AV adoption in the future commercial market. The automated driving system (ADS) that replaces human drivers should be perceived as very useful before the latter are willing to give up their control and entrust their lives to the ADS. However, compared with the growing number of studies on public acceptance of AVs, there has been limited research focusing on user experience and usability. We examined AV and ADS user experience and usability, ADS failures’ influence on them, and their influences on re-riding willingness. We conducted a field study using a real AV and a large-scale test track. We invited participants (N = 261) to travel in the AV as passengers in a low-speed environment. Participants were randomly assigned into the normal condition or the fault condition (its participants were exposed to an ADS failure). We measured participants’ positive experience (feeling relaxed, safe, and comfortable) and negative experience (feeling tense and risky) while riding in the AV and perceived usability of the ADS based on the System Usability Scale. In both conditions, participants reported moderate positive experience and perceived usability but a relatively high level of willingness to ride in our AV again. The ADS failure reduced positive experience and perceived usability, and it increased negative experience. Positive experience and perceived usability, but not negative experience, influenced re-riding willingness. Compared with male participants, female participants reported less positive experience and lower perceived usability. We discuss implications of our results as well as limitations of this research.
... From that, the need to investigate efficient and effective interaction arises [29]. Furthermore, it might not be sufficient to develop interfaces that users are satisfied with [34], but which they have fun and enjoy interacting with [32,148]. Moreover, safety critical issues of AD, like TORs in SAE L3, also impact users overall driving experience [149]. ...
During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.
... Regarding the automotive industry with its long product lifecycles [6], its different touch-points, and diverse and global user base [21] the question of how to capture an 'overall' UX score [26] gets even more interesting. Additionally, Frison et al. emphasize the importance of UX, as it influences trust in the vehicle [13] and gets even more important with the transition toward automated driving [12]. ...
We are interested in the role of field user interaction data in the development of In-Vehicle Information Systems (IVISs), the potentials practitioners see in analyzing this data, the concerns they share, and how this compares to companies with digital products. We conducted interviews with 14 UX professionals, 8 from automotive and 6 from digital companies, and analyzed the results by emergent thematic coding. Our key findings indicate that implicit feedback through field user interaction data is currently not evident in the automotive UX development process. Most decisions regarding the design of IVISs are made based on personal preferences and the intuitions of stakeholders. However, the interviewees also indicated that user interaction data has the potential to lower the influence of guesswork and assumptions in the UX design process and can help to make the UX development lifecycle more evidence-based and user-centered. CCS CONCEPTS • General and reference → Surveys and overviews; • Human-centered computing → HCI design and evaluation methods; Empirical studies in HCI. KEYWORDS interview study, user experience, in-vehicle information systems ACM Reference Format:
... Regarding the automotive industry with its long product lifecycles [6], its different touch-points, and diverse and global user base [21] the question of how to capture an 'overall' UX score [26] gets even more interesting. Additionally, Frison et al. emphasize the importance of UX, as it influences trust in the vehicle [13] and gets even more important with the transition toward automated driving [12]. ...
We are interested in the role of field user interaction data in the development of IVIS, the potentials practitioners see in analyzing this data, the concerns they share, and how this compares to companies with digital products. We conducted interviews with 14 UX professionals, 8 from automotive and 6 from digital companies, and analyzed the results by emergent thematic coding. Our key findings indicate that implicit feedback through field user interaction data is currently not evident in the automotive UX development process. Most decisions regarding the design of IVIS are made based on personal preferences and the intuitions of stakeholders. However, the interviewees also indicated that user interaction data has the potential to lower the influence of guesswork and assumptions in the UX design process and can help to make the UX development lifecycle more evidence-based and user-centered.
The widespread implementation of mobile personal computing devices like notebooks and smartphones has changed knowledge work towards more mobility beyond the traditional office desk. Rising levels of driving automation on the road may initiate a similar shift. By changing the driver's role to that of the \emph{driver-passenger}, the demand for so-called \glspl{NDRT} grows. For example, commuters could use their time on the road to prepare for the upcoming office day, or truck drivers could do logistics planning between on- and offloading. However, driver-passengers still have the responsibility to stay ready to respond to \glspl{TOR}. They occur when a not-yet fully automated vehicle experiences a system failure or functional limitation.
Accordingly, in this thesis, we investigate the concept of a mobile office in a \gls{SAE L3} vehicle. Its goals are to enable productive \gls{NDRT} engagement during automated driving phases but also safe manual driving after \glspl{TOR}. Therefore, user interfaces that face these challenges for the typical office tasks of text entry and comprehension in \gls{SAE L3} vehicles are developed and evaluated. They account for both office work and \gls{TOR}/driving ergonomics issues based on the user-centered design process. The designs are informed by standards, applied \gls{HCI} research literature, and cognitive resource and multitasking theories. Mixed-methods user studies with medium- to high-fidelity prototypes allowed quantitatively and qualitatively assessing the interfaces and their features regarding users' objective and subjective performance with them and physiological responses to them. Thereby, we inferred generalizable results on the design features, underlying theories, and the methods used to design and evaluate them.
We found that merging knowledge from various areas of \gls{HCI} can promote safety and productivity of office work in \gls{SAE L3} vehicles to some extent when iteratively improving interface designs. Furthermore, the mixed-methods evaluations revealed detailed aspects of applying prevalent \gls{HCI} theory and applied research findings in a novel and complex domain. Overall, we report findings on various mobile office interface modalities and combinations concerning their impact on ergonomics factors such as performance, workload, situational awareness, and well-being. Additionally, we detail the methodological approach taken, including the infrastructure required to implement it.
In recent years, the goal of companies to retain customers through good usability has evolved into a more holistic view to enhance the user experience. The purely pragmatic view is to be extended by hedonic aspects in order to touch the users also on the emotional level. Although everyone talks about user experience (UX), it still seems to be just “old wine in new bottles”. Despite extensive UX theory research in recent years, UX is still often used as a synonym for usability. Due to increasing vehicle automation, the automotive industry now also has to rethink its (long) existing processes and develop new strategies in order to keep its customers loyal to the brand in the future. Traffic will change fundamentally—and drivers will often neither drive themselves nor own a vehicle. With this book chapter we want to create the basis for this transformation process. After an overview of the current state of UX practice in the development of user interfaces for vehicle automation, the topic is systematically unfolded from the perspective of academia (literature studies) and industry (expert interviews). Based on the findings, the “DAUX framework” is presented as part of a need-centered development approach. It serves as a structured guide on how to define and evaluate UX in consideration of the challenges of automated driving. For this purpose, it provides guidelines on how (a) relevant needs for hypotheses/UI concept development can be identified and (b) UX can be evaluated by triangulating behavioral-, product-, and experience-oriented methods. To demonstrate its potential, the framework is applied in three case studies, each addressing a different level of automation (SAE L2, SAE L3, and SAE L4). This demonstrates that the “DAUX framework” promotes a holistic view of UX to encourage the development of UIs for driving automation. In particular, it is intended to help resolve technical constraints faced by designers and developers in the different levels of automation with the aim to create a positive UX.
A growing body of Human-Computer-Interaction research and the automotive industry has identified that understanding user needs and creating positive user experience (UX) is crucial in order to successfully introduce Autonomous Driving (AD) vehicles to the market. AD research is commonly undertaken to provide user insights by studying the individual-technology experiences in lab settings or by forecasting attitudes and acceptability through large surveys. However, these approaches base their knowledge on people's past or present expectations and limited real life experiences of AD. To better understand upcoming individual user needs and to enable new innovations beyond acceptability forecasts and UX lab tests, we need to identify new concepts through alternative methodologies that can generate user foresights based on users’ evolving anticipations of AD in their everyday lives. We propose an ethnographic approach with iterative speculative scenarios, which we demonstrate through a study undertaken with participants from five families who were introduced to evolving levels of AD, in real-life situations. To demonstrate the methodology, we draw on empirical findings which reveal anticipatory experiences, which we abstract through the concepts of confidence, hope and being-in-the-moment. We show how these concepts structured our user foresights, and outline the implications of engaging them in innovation processes.
Without public support, the deployment of Automated Vehicles (AVs) is doomed to failure. Therefore, their acceptability has been widely studied. On the basis of a systematic literature review of 113 empirical studies, the present paper studies the influence of the modes of contact with the AV (no explanation provided, written or pictorial description only, simulator, real experience) on sociodemographic variables, acceptability, behavioural intention and their determinants. The analysis of the dependent variables shows a lack of clarification of what is actually measured and a heterogeneity of measurements that makes comparisons difficult. The determinants were separated in two groups: preferences, which refer to projections in use, and perceptions, which refer to beliefs. The determinants in the preferences group, and some determinants in the perceptions group (perceived ease of use and usefulness and attitudes), are not influenced by the mode of contact with the AV. Indeed, a more concrete mode of contact does not change the participants’ responses for these variables. For the other determinants of the perceptions group, the mode of contact with AVs impacts participants’ responses. Trust and perceived safety are influenced by the mode of contact. More significantly, we observed that the relationship between acceptability and the level of knowledge or control was moderated by the mode of contact with AVs. These results lead us to encourage research favouring direct experience with the AV to study its acceptability. A discussion of these recurring results but also the significant methodological and theoretical shortcomings highlighted by the review is presented such as the research gaps in terms of sampling or statistical robustness. In conclusion, a few methodological approaches are proposed for the study of technological devices that are little or not known to future users, as well as a reflection on the usefulness of studying the acceptability of these little-known devices.
Major improvements in autonomous driving allow the realization of automated valet parking. However, parking is known to be a challenging and complex task with high cognitive demand, and many challenges remain within the area of human factors, for example, whether the user trust automated driving vehicle systems and whether the system is understandable and safe. Therefore, we present a need-centered approach and the involved “UX framework”, which can optimize user experience in the automated valet parking system. We create Persona models through surveys and interviews to explore pain points and users’ needs, and use Kano model to categorize and prioritize these needs from three levels: security, understandability, and controllability, then reflect them in each function of the system to build the information architecture. Meanwhile, we also consider the corresponding services about One-dimensional and Attractive quality. Furthermore, we put forward the “UX framework” and 5 principles for the interaction design of the automated valet parking system, which will provide references for designers and practitioners.
Today's developments in the automotive industry are moving towards automated driving. At the highest levels, the driver becomes the passenger, which presents a new challenge for human-computer interaction.
People not only have to trust in the automated system but are also confronted with increased complexity, as it is often not clear what the automated vehicle is about to do.
An ambient light display is one way to give the driver a clearer picture of the car's intentions and to keep complexity low.
We have examined the impact on trust and user experience in more detail using two concepts for an ambient light display.
One design provides information about detected potential conflicts in the current trajectory. The other design also highlights the future trajectory.
We implemented both concepts as virtual light bars at the bottom of the screens.
We evaluated them in a fixed-base driving simulator with 18 participants against each other and a baseline condition without additional information.
Our scenario is a fully automated journey (SAE Level 5) through a German town.
Although the two concepts do not differ much from each other, only the display showing both information - possible conflicts and future driving route - provided a clear added value for the users.
Overreliance in technology is safety-critical and it is assumed that this could have been a main cause of severe accidents with automated vehicles. To ease the complex task of permanently monitoring vehicle behavior in the driving environment, researchers have proposed to implement reliability/uncertainty displays. Such displays allow to estimate whether or not an upcoming intervention is likely. However, presenting uncertainty just adds more visual workload on drivers, who might also be engaged in secondary tasks. We suggest to use olfactory displays as a potential solution to communicate system uncertainty and conducted a user study (N=25) in a high-fidelity driving simulator. Results of the experiment (conditions: no reliability display, purely visual reliability display, and visual-olfactory reliability display) comping both objective (task performance) and subjective (technology acceptance model, trust scales, semi-structured interviews) measures suggest that olfactory notifications could become a valuable extension for calibrating trust in automated vehicles.
Technology acceptance is a critical factor influencing the adoption of automated vehicles. Consequently, manufacturers feel obliged to design automated driving systems in a way to account for negative effects of automation on user experience. Recent publications confirm that full automation will potentially lack in the satisfaction of important user needs. To counteract, the adoption of Intelligent User Interfaces (IUIs) could play an important role. In this work, we focus on the evaluation of the impact of scenario type (represented by variations of road type and traffic volume) on the fulfillment of psychological needs. Results of a qualitative study (N=30) show that the scenario has a high impact on how users perceive the automation. Based on this, we discuss the potential of adaptive IUIs in the context of automated driving. In detail, we look at the aspects trust, acceptance, and user experience and its impact on IUIs in different driving situations.
Automated driving eliminates the permanent need for vehicle control and allows to engage in non-driving related tasks. As literature identifies office work as one potential activity, we estimate that advanced input devices will shortly appear in automated vehicles. To address this matter, we mounted a keyboard on the steering wheel, aiming to provide an exemplary safe and productive working environment. In a driving simulator study (n=20), we evaluated two feedback mechanisms (heads-up augmentation on a windshield, conventional heads-down display) and assessed both typing effort and driving performance in handover situations. Results indicate that the windshield alternative positively influences handovers, while heads-down feedback results in better typing performance. Text difficulty (two levels) showed no significant impact on handover time. We conclude that for a widespread acceptance of specialized interfaces for automated vehicles, a balance between safety aspects and productivity must be found in order to attract customers while retaining driving safety.
Objective:
The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation.
Background:
Securing driver engagement-by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions-is a major challenge in the human factors literature.
Method:
One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag.
Results:
Supervision reminders effectively maintained drivers' eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag).
Conclusion:
The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel.
Application:
Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.
User experience (UX) evaluation is a growing field with diverse approaches. To understand the development since previous meta-review efforts, we conducted a state-of-the-art review of UX evaluation techniques with special attention to the triangulation between methods. We systematically selected and analyzed 100 papers from recent years and while we found an increase of relevant UX studies, we also saw a remaining overlap with pure usability evaluations. Positive trends include an increasing percentage of field rather than lab studies and a tendency to combine several methods in UX studies. Triangulation was applied in more than two thirds of the studies, and the most common method combination was questionnaires and interviews. Based on our analysis, we derive common patterns for triangulation in UX evaluation efforts. A critical discussion about existing approaches should help to obtain stronger results, especially when evaluating new technologies.
The rise of evermore autonomy in vehicles and the expected introduction of self-driving cars have led to a focus on human interactions with such systems from an HCI perspective over the last years. Automotive User Interface researchers have been investigating issues such as transition control procedures, shared control, (over)trust, and overall user experience in automated vehicles. Now, it is time to open the research field of automated driving to other CHI research fields, such as Human-Robot-Interaction (HRI), aeronautics and space, conversational agents, or smart devices. These communities have been dealing with the interplay between humans and automated systems for more than 30 years. In this workshop, we aim to provide a forum to discuss what can be learnt from other domains for the design of autonomous vehicles. Interaction design problems that occur in these domains, such as transition control procedures, how to build trust in the system, and ethics will be discussed.
Autonomous vehicles have the potential to fundamentally change existing transportation systems. Beyond legal concerns, these societal evolutions will critically depend on user acceptance. As an emerging mode of public transportation [7], Autonomous mobility on demand (AMoD) is of particular interest in this context. The aim of the present study is to identify the main components of acceptability (before first use) and acceptance (after first use) of AMoD, following a user experience (UX) framework. To address this goal, we conducted three workshops (N=14) involving open discussions and a ride in an experimental autonomous shuttle. Using a mixed-methods approach, we measured pre-immersion acceptability before immersing the participants in an on-demand transport scenario, and eventually measured post-immersion acceptance of AMoD. Results show that participants were reassured about safety concerns, however they perceived the AMoD experience as ineffective. Our findings highlight key factors to be taken into account when designing AMoD experiences.
Vehicle automation is expected to change users' driving experience dramatically. Interactions with the car, like accelerating, braking, and overtaking will be inherited by technical systems. As a consequence, users' basic psychological needs of feeling competent and autonomous will be restricted. This might reduce as well pleasure or fun of driving. In our video, we are presenting the vehicle interface "Hotzenplotz", which combines the advantages of both manual and automated driving to foster the joy of driving. By the optional possibility for accelerating, braking, and overtaking, users feel stimulated, and significantly more competent and autonomous as compared to purely automated driving.
Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the "human factor" is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.
Inappropriate trust in the capabilities of automated driving systems can result in misuse and insufficient monitoring behaviour that impedes safe manual driving performance following takeovers. Previous studies indicate that the communication of system uncertainty can promote appropriate use and monitoring by calibrating trust. However, existing approaches require the driver to regularly glance at the instrument cluster to perceive the changes in uncertainty. This may lead to missed uncertainty changes and user disruptions. Furthermore, the benefits of conveying the uncertainty of the different vehicle functions such as lateral and longitudinal control have yet to be explored. This research addresses these gaps by investigating the impact of unobtrusive and function-specific feedback on driving safety and user experience. Transferring knowledge from other disciplines, several different techniques will be assessed in terms of their suitability for conveying uncertainty in a driving context.
This workshop intends to address contemporary issues
surrounding trust in technology in the challenging and constantly
changing context of automated vehicles. In particular,
this workshop focuses on two main aspects: (1) appropriate
definitions of trust and associated concepts for
the automated driving context, especially regarding trust
calibration in individual capabilities versus overall trust; (2)
appropriate measures (qualitative and quantitative) to quantify
trust in automated vehicles and in-vehicle interfaces.
The workshop proceeds on the basis of a keynote and accepted
position papers byparticipants as a basis for the
focused breakout sessions. The outcome of the workshop
will become the basis for a subsequent joint publication of
organizers and participants discussing the issues (1) and
(2).
In this paper, a taxonomy of handover and handback (i.e., from manual to automatic control and vice versa) is proposed to be used by practitioners and researchers to help assure the duration of those periods are clearly defined, and accordingly, studies examining them are comparable and have repeatable results. Furthermore, use of this framework will help assure that those implementing automation will do so in a comprehensive manner. The taxonomy is more detailed than that in SAE Standard J3114. Handover includes the phases preparation, perception (of the handover signal), suspension (of in-vehicle tasks) and the actual process of taking over, which can be subdivided into sufficient (to steer and control speed) and full (where situation awareness is complete) control. Furthermore, handover can be imminent, scheduled, or user-initiated. For handback, the phases are initialization, the actual handback, and re-engagement (of the driver). Handback may be optional or mandatory and user-or system initiated. For both handover and handback processes, the duration and change of the control transfer (as a function of time) needs to be precisely described/specified.
Automated vehicles, but also safety or driver
assistance systems for manually driven cars, will soon face
situations where they have to choose between several options
with negative or even lethal outcome for the one or the
other party. Experimental ethics is an approach to evaluate
expectations humans put into the morality of digital agents.
With this work we present a personalized abstraction of the
“Trolley Problem” evaluated in a driving simulator. The aim of
the study was to assess drivers’ individual attitudes in ethical
decisions and derive common knowledge about how to solve
such situations. In contrast to previous work, the study at hand
looks at the problem from a holistic point of view, including
uncertainty and accident risk (presented to subjects as their
probability to survive). Furthermore, subjective scales and semistructured
interviews to determine subjects’ justifications for
ethical decisions complemented the setting. Our results (n=40)
suggest that most drivers want their vehicles to act in an
utilitarian way and opt for the more severe collision - even when
their own probability to survive is substantially low. In addition,
age and size of the injured party have a significant effect
on the results. Qualitative data (interviews) indicate that the
justification, in particular for decisions with the same outcome,
strongly differs as most people have embodied their own moral
concepts.
It is expected that automated vehicles (AVs) will only be used when customers believe them to be safe, trustworthy, and match their personal driving style. As AVs are not very common today, most previous studies on trust, user experience, or acceptance measures in automated driving are based on qualitative measures. The approach followed in this work is different, as we compared the direct effect of human drivers versus automated driving systems (ADSs) on the front seat passenger. In a driving simulator study (N=48), subjects had either to ride with an ADS, a male, or a female driver. Driving scenarios were the same for all subjects. Findings from quantitative measurements (HRV, face tracking) and qualitative pre-/post study surveys and interviews suggest that there are no significant differences between the passenger groups. Our conclusion is, that passengers are already inclined to accept ADS and that the market is ready for AVs.
The interaction between drivers and their cars will change significantly with the introduction of autonomous vehicles. The driver's role will shift towards a supervisory control of their autonomous vehicle. The eventual relief from the driving task enables a complete new area of research and practice in human-computer interaction and interaction design. In this one-day workshop, participants will explore the opportunities the design space of autonomous driving will bring to HCI researchers and designers. On the day before workshop participants are invited to visit (together with workshop organizers) Google Partnerplex and Stanford University. At Google participants will have the opportunity to explore Google's autonomous car simulator and might have the chance to experience one of the Google Cars (if available). At Stanford participants are invited to ride in a Wizard-of-Oz autonomous vehicle. Based on this first-hand experience we will discuss design approaches and prototype interaction systems during the next day's workshop. The outcome of this workshop will be a set of concepts, interaction sketches, and low-fidelity paper prototypes that address constraints and potentials of driving in an autonomous car.
The objective of this study is to examine the user’s adoption aspects of autonomous vehicle, as well as to investigate what factors drive people to trust an autonomous vehicle. A model explaining the impact of different factors on autonomous vehicles’ intention is developed based on the technology acceptance model and trust theory. A survey of 552 drivers was conducted and the results were analyzed using partial least squares. The results demonstrated that perceived usefulness and trust are major important determinants of intention to use autonomous vehicles. The results also show that three constructs—system transparency, technical competence, and situation management—have a positive effect on trust. The study identified that trust has a negative effect on perceived risk. Among the driving-related personality traits, locus of control has significant effects on behavioral intention, whereas sensation seeking did not. This study investigated that the developed model explains the factors that influence the acceptance of autonomous vehicle. The results of this study provide evidence on the importance of trust in the user’s acceptance of an autonomous vehicle.
[The first two authors, Jae-Gil Lee and Ki Joon Kim, contributed equally to this study]
Although autonomous vehicles are increasingly becoming a reality, eliminating human intervention from driving may imply significant safety and trust-related concerns. To address this issue from a psychological perspective, this study applies layers of anthropomorphic cues to an artificial driving agent and explicates the process in which these cues promote positive evaluations and perceptions of an unmanned driving system. In a between-subjects factorial experiment (N = 89) consisting of three unmanned driving scenarios, participants interacted with an artificial driving agent with different levels of anthropomorphic cues induced by the variations in appearance (human-like vs. gadget-like) and autonomy (high vs. low) of the agent. The results indicated that human-like appearance and high autonomy were more effective in eliciting positive perceptions of the agent. In addition, a mediation analysis revealed that the greater level of anthropomorphism induced by human-like appearance and high autonomy in the agent evoked the feelings of social presence, which in turn positively affected the perceived intelligence and safety of and trust in the agent, suggesting that the extent to which users perceive the driving agent as intelligent, safe, and trustworthy is largely determined by the feelings of social presence experienced during their interaction.
We collected over a thousand technology-mediated positive experiences with media and obtained measures describing aspects of the experience itself (affect, psychological need fulfillment) and of the product (i.e., content and technology) integral to the experience (pragmatic quality, hedonic quality). We found a strong relation between intensity of need fulfillment and positive affect. Furthermore, different activities had different need profiles. Watching was foremost a relatedness experience, listening a stimulation and meaning experience, and playing a competence experience. Need fulfillment and positive affect was related to perceptions of hedonic quality, however moderated through attribution, that is, the belief that the product played a role in creating the experience. Pragmatic quality was not linked to experiential measures. The present study (a) demonstrates the merits of distinguishing between an experience-oriented and a product-oriented evaluation, (b) suggests a set of possible measurement instruments for experience-oriented and a product-oriented evaluation, and (c) details the process of how positive experience is transformed into positive product perceptions and judgments of appeal.
This article investigates Herzberg's [1959] notion of hygienes, factors contributing to dissatisfaction but not to satisfaction, and motivators, factors contributing to satisfaction but not to dissatisfaction, in the context of user experience (UX). Earlier work has theorized that the notion of hygienes and motivators applies to UX but has neither shown empirical evidence for this theory nor exemplified what such factors would look like in UX. We adapt Herzberg's methodology to analyze 303 events where users felt good or bad about their smartphone and derive factors that may work as hygienes or motivators. We identified technical quality and price as hygienes, and utility and convenience as motivators. These factors do not correspond to those mentioned as typical examples of hygienes and motivators in the UX literature (i.e., instrumental qualities such as usability for hygienes and non-instrumental qualities such as beauty for motivators). We discuss this discrepancy in the context of pragmatic and hedonic quality and psychological need fulfillment.
Autonomous vehicles have gained attention recently since research organizations and companies have presented (semi-) autonomous vehicles driving in public traffic. This workshop covers the crossover between driving/riding in (semi-) autonomous vehicles and user experience (UX) research. The focus lies in an in-depth discussion on challenges and potentials for UX in autonomous vehicles. We will explore various areas such as, user experience factors, interaction design issues, entertainment potentials, social driving, and methodological issues. Additionally, we envision building a bridge between the automotive community and the human-robot-interaction community, since we believe autonomous vehicles can be regarded as a very specific kind of a robot. The overall aim of the workshop is to discuss the future landscape for research within and across each of these areas.
Automation permeates everyday life in the disguise of fully-automated coffee makers, dishwashers, or self-driving cars. While being convenient, such automation may have detrimental effects on the experience gained through the (semi-)automated activity. This paper argues to be more sensitive to the experiential costs of everyday automation. To this end, it provides an in-depth quantitative and qualitative comparison of a more automated and a more manual way of brewing coffee. Brewing coffee manually was more positive and more need fulfilling. This was due to the more intense experience of competence and stimulation. Automation focused people on the outcome. The process became meaningless, degraded to “waiting time.” Overall, the experience became “flat”, significantly less meaningful and enjoyable, but also less demanding. Automation turned a potentially experience-rich activity into something less satisfactory for the sake of convenience. Since we believe that technology should make everyday activities experientially richer rather than “designing them away,” we discuss the emerging challenges for an experiential design of everyday automation.
This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record. On the ground, a variety of driver assistance systems are being developed, and highly automated vehicles with increasingly autonomous capabilities are becoming possible. Human-centred automation has paved the way for a better cooperation between automation and humans. How can these highly automated systems be structured so that they can be easily understood, how will they cooperate with the human? The presented research was conducted using the methods of iterative build-up and refinement of framework by triangulation, i.e. by instantiating and testing the framework with at least two derived concepts and prototypes. This article sketches a general, conceptual ergonomic framework of cooperative guidance and control of highly automated vehicles, two concepts derived from the framework, prototypes and pilot data. Cooperation is exemplified in a list of aspects and related to levels of the driving task. With the concept 'Conduct-by-Wire', cooperation happens mainly on the guidance level, where the driver can delegate manoeuvres to the automation with a specialised manoeuvre interface. With H-Mode, a haptic-multimodal interaction with highly automated vehicles based on the H(orse)-Metaphor, cooperation is mainly done on guidance and control with a haptically active interface. Cooperativeness should be a key aspect for future human-automation systems. Especially for highly automated vehicles, cooperative guidance and control is a research direction with already promising concepts and prototypes that should be further explored. The application of the presented approach is every human-machine system that moves and includes high levels of assistance/automation.
Cars play an important role in many individual and social practices, often resulting in positive experiences, such as the freedom of the daily commute, the joys of intensive conversations, or the excitement of a discovered place. In this paper, we use the lens of Experience Design, -- particularly the notion that meaning and positivity is related to the fulfillment of universal psychological needs -- to explore the potential of an experience-oriented approach to design for interactivity in and through cars. Examples of designing for competence, autonomy, relatedness, security, stimulation, and popularity open up a space for novel articulations of cars. We show how each need is already apparent in car-related practices and explore how focusing on a particular need will become apparent in specific conceptual sketches.
User Experience (UX) is not just "old wine in new bottles". It is a truly extended and distinct perspective on the quality of interactive technology: away from products and problems to humans and the drivers of positive experience. This paper will present my particular perspective on UX and will discuss its implications for the field of Human-Computer Interaction.
Recent research in user experience (UX) has studied narratives, users' account of their interaction with technology. It has emphasized specific constructs (e.g., affect, needs, hedonics) and their interrelation, but rarely analyzed the content of the narratives. We analyze the content and structure of 691 user-generated narratives on positive and negative experiences with technology. We use a multi-method approach consisting of manual (structural analysis of narratives) as well as of automated content analysis methods (psycholinguistic analysis and machine learning). These analyses show converging evidence that positive narratives predominantly concern social aspects such as family and friends. In addition, technology is positively experienced when it enables users to do things more efficiently or in a new way. In contrast, negative narratives often express anger and frustration due to technological failures. Our multi-method approach illustrates the potential of automated (as opposed to manual) content analysis methods for studying text-based experience reports.
In this paper, we introduce a general framework for product experience that applies to all affective responses that can be experienced in human-product interaction. Three distinct components or levels of product experiences are discussed: aesthetic experience, experience of meaning, and emotional experience. All three components are distinguished in having their own lawful underlying process. The aesthetic level involves a product’s capacity to delight one or more of our sensory modalities. The meaning level involves our ability to assign personality or other expressive characteristics and to assess the personal or symbolic significance of products. The emotional level involves those experiences that are typically considered in emotion psychology and in everyday language about emotions, such as love and anger, which are elicited by the appraised relational meaning of products. The framework indicates patterns for the processes that underlie the different types of affective product experiences, which are used to explain the personal and layered nature of product experience
To practitioner and researcher alike, consumer values play an important role in understanding behavior in the marketplace. This paper presents a model linking perceived product attributes to values.
Today's vehicles have myriad user interfaces, from those related to the moment-to-moment control of the vehicle, to those that allow the consumption of information and entertainment. The bulk of the research in this domain is related to manual driving. With recent advances in automated vehicles, there is an increased attention to user interactions as they relate to automated vehicles. In exploring humanmachine interaction for both manual and automated driving, a key issue has been how to create safe in-vehicle interactions that assist the driver in completing the driving task, as well as to allow drivers to accomplish various non-driving tasks. In automated vehicles, human-machine interactions will increasingly allow users to reclaim their time, so that they can spend time on non-driving tasks. Given that it is unlikely that most vehicles will be fully automated in the near future, there are also significant efforts to understand how to help the driver switch between different modes of automation. This paper provides a review of these areas of research, as well as recommendations for future work.
As technical realization of highly and fully automated vehicles draws closer, attention is being shifted from sheer feasibility to the question of how an acceptable driving style and thus comfort can be implemented. It is increasingly important to determine, how highly automated vehicles should drive to ensure driving comfort for the now passive drivers. Thus far, only little research has been conducted to examine this issue. In order to lay a basis on how automated vehicles should drive to ensure passenger comfort, different variations of three central maneuvers were rated and analyzed. A simulator study (N = 72) was conducted in order to identify comfortable driving strategies. Three variations of lane changes, accelerations and decelerations were configured by manipulating acceleration and jerk, and thus the course of each maneuver. Furthermore, the influence of personality traits and self-reported driving style on preferences of differently executed automated maneuvers was analyzed. Results suggest keeping acceleration and jerk as small as possible for acceleration maneuvers. For lane changes, both small accelerations as well as an early motion feedback are advisable. Interestingly, decelerating as a manual driver would is rejected compared to two artificial alternatives. Moreover, no influence of personality traits on maneuver preference was found. Only self-reported driving style had a marginal effect on participants' preferences. In conclusion, a recommendation for an automated driving style can be given, which was perceived as comfortable by participants regardless of their personality.
Vehicle automation fundamentally changes the relationship with our vehicles whereby system trust and occupant comfort will become key user requirements. We here explored what information users expect to receive across a wide range of traffic scenarios in urban and peri-urban environments for both highly and fully automated vehicles. Results indicated that users expected the HMI to provide information to promote system transparency, comprehensibility, and predictability. Visual presentation using Head Up Display technologies was the preferred method but older users in particular expressed a preference for auditory interfaces. In hazardous or uncertain situations, participants expressed a desire to be able to override or confirm vehicle actions. Results indicated that information requirements might however change with experience signifying the potential role for adaptive and configurable HMI solutions for future vehicles.
Automotive interaction design is undergoing a major shift due to the disruptive forces of automation and information technology. This paper reviews current challenges in human vehicle interaction design and argues that these challenges demand that interaction become a primary consideration in designing automotive user experiences. We survey exploratory interaction design techniques for human vehicle interactions, showing examples from our research of each technique in action. The techniques are enactments, contextual inquiry, scale scenarios, Wizard of Oz, field experiments and video and animation prototyping. We reflect upon our experiences with these methods, and discuss as yet unmet needs in interaction design for the road ahead.
As long as automated vehicles are not able to handle driving in every possible situation, drivers will still have to take part in the driving task from time to time. Recent research focused on handing over control entirely when automated systems reach their boundaries. Our overview on research in this domain shows that handovers are feasible, however, they are not a satisfactory solution since human factor issues such as reduced situation awareness arise in automated driving. In consequence, we suggest to implement cooperative interfaces to enable automated driving even with imperfect automation. We recommend to consider four basic requirements for driver–vehicle cooperation: mutual predictability, directability, shared situation representation, and calibrated trust in automation. We present research that can be seen as a step towards cooperative interfaces in regard to these requirements. Nevertheless, these systems are only solutions for parts of future cooperative interfaces and interaction concepts. Future design of interaction concepts in automated driving should integrate the cooperative approach in total in order to achieve safe and comfortable automated mobility.
In this paper, we investigate which non-driving-related activities drivers want to perform while driving highly or fully automated. Beyond the available advanced driving assistance functions, we expect that highly automated driving will soon be available in production vehicles. While many technological aspects have been investigated, it is not yet clear (a) which activities the drivers want to perform once they do not have to steer or monitor their car any more and (b) which of those will be feasible. In contrast to prior (survey-based) research, we investigate the driver's needs for such activities by employing a combination of a web survey, in-situ observations, and an in-situ survey. Also, we have a look at the specific requirements of the European / German market in contrast to prior research conducted mostly for English-speaking countries.
The findings indicate that besides traditional activities (talking to passengers, listening to music), daydreaming, writing text messages, eating and drinking, browsing the Internet, and calling are most wanted for highly automated driving. This shows the potential for mobile and ubiquitous multimedia applications in the car.
Technology is able to free people from daily chores and to empower activities, which had not been possible before. At the same time, technology, especially automation, causes many mundane and potentially meaningful activities to disappear. In this sense, each convenient "enhancement" could also be understood as an experiential "amputation." This paper presents an exploration of an approach to automation, which seeks to reconcile the advantages of automation with meaningful experience. Hotzenplotz is an electrical coffee grinder combined with a functionally unnecessary manual interaction to create meaning. We empirically compared Hotzenplotz to an off-the-shelf manual and electrical grinder and found the resulting experience to be more fulfilling than the electric grinder and at least as fulfilling as the manual grinder, in terms of, for example, affective experience, meaning, and fulfillment of psychological needs. Hotzenplotz as a viable example of the subtle role an experience-oriented approach to the design of technology can play in shaping our everyday lives.
Acceptance of highly and fully automated vehicles will depend on system trust and the ability to comfortably engage in non-driving related tasks (NDRT). We here hypothesize a potential trade-off between the two. The paper describes the development of two UI concepts based on trust factors derived from the literature and benchmarking of current (concept) UIs to explore the balance between trust and engagement in NDRT. The concepts were inspired by the Valeo Mobius User Interface concept. The level of intrusiveness of trust related information was the key parameter differentiating the two concepts and was manipulated by adopting a single head up display versus a distributed display configuration also including a central console display. A comparative simulator study is underway to explore the balance between trust and comfortable engagement in NDRT and areas of future research are discussed.
AUTONOMOUS CARS PROMISE to give us back the time we spend in traffic, improve the flow of traffic, reduce accidents, deaths, and injuries, and make personal car travel possible for everyone regardless of their abilities or condition. But despite impressive demonstrations and technical advances, many obstacles remain on the road to fully autonomous cars.20 Overcoming the challenges to enabling autonomous cars to safely operate in highly complex driving situations may take some time. Manufacturers already produce partially automated cars, and a spirited competition to deliver the most sophisticated ones is under way. Cars that provide high levels of automation in some circumstances (such as highway driving) have already arrived in the marketplace and promise to be in the hands of a large number of car owners in the next few years.
User Experience (UX) is a major concept in HCI and a variety of different UX definitions have been suggested within the scientific community. An ISO UX definition has been presented to standardize the term from an industry perspective. We introduce methods from formal logic in order to formalize and analyze the ISO UX definition with regard to consistency and ambiguities and present recommendations for an improved version. Although this kind of formalization is not common within the CHI community, we show that quasi-formal methods provide an alternative way of analyzing widely discussed HCI terms, such as UX, to deepen its understanding.
The field of automotive user interfaces has developed rapidly over the last several years. To date, the field has primarily focused on creating user interfaces that promote safe driving, including when the driver is engaged in a secondary task in addition to operating the vehicle. However, researchers now need to prepare for a major change in the automotive domain: the automated driving revolution. The authors argue for a new research agenda that focuses on four challenges for automotive user interfaces: assuring safety in the age of automation, transforming vehicles into places for productivity and play, taking advantage of new mobility options made possible by automated vehicles, while throughout all this preserving user privacy and data security. This article is part of a special issue on smart vehicle spaces.
User experience (UX) research has expanded our notion of what makes interactive technology good, often putting hedonic aspects of use such as fun, affect, and stimulation at the center. Outside of UX, the hedonic is often contrasted to the eudaimonic, the notion of striving towards one's personal best. It remains unclear, however, what this distinction offers to UX research conceptually and empirically. We investigate a possible role for eudaimonia in UX research by empirically examining 266 reports of positive experiences with technology and analyzing its relation to established UX concepts. Compared to hedonic experiences, eudaimonic experiences were about striving towards and accomplishing personal goals through technology use. They were also characterized by increased need fulfillment, positive affect, meaning, and long-term importance. Taken together, our findings suggest that while hedonic UX is about momentary pleasures directly derived from technology use, eudaimonic UX is about meaning from need fulfilment.
It is expected that autonomous vehicles capable of driving without human supervision will be released to market within the next decade. For user acceptance, such vehicles should not only be safe and reliable, they should also provide a comfortable user experience. However, individual perception of comfort may vary considerably among users. Whereas some users might prefer sporty driving with high accelerations, others might prefer a more relaxed style. Typically, a large number of parameters such as acceleration profiles, distances to other cars, speed during lane changes, etc., characterize a human driver's style. Manual tuning of these parameters may be a tedious and error-prone task. Therefore, we propose a learning from demonstration approach that allows the user to simply demonstrate the desired style by driving the car manually. We model the individual style in terms of a cost function and use feature-based inverse reinforcement learning to find the model parameters that fit the observed style best. Once the model has been learned, it can be used to efficiently compute trajectories for the vehicle in autonomous mode. We show that our approach is capable of learning cost functions and reproducing different driving styles using data from real drivers.
Autonomous cars are undergoing rapid development. In order to gain acceptance and adoption, there is a need to understand how users will respond to the technology, and what possible benefits or difficulties they perceive. A prerequisite for eliciting this information is the involvement of users. Having said that, several studies have demonstrated the problems associated with investigating 'the future'. In a pilot study, two different qualitative approaches for exploring users' expectations for future automotive technology were applied in two studies. The first study encompassed drawing, collaging and interviewing while the second study used drawing and enactment of expected use to mediate a shift from today to tomorrow. A content analysis revealed that the first study provided information on social implications, trust and every-day activities in autonomous cars, whereas the second study provided further in-depth details about the interaction and interior design expectations, as well as trust and anticipated activities. The results contribute to the author's knowledge on how different studies of the same topic can elicit different types of data.
Surveys [8] show that people generally have a positive attitude towards autonomous cars. However, these studies neglect that cars have different levels of autonomy and that User Acceptance (UA) and User Experience (UX) with autonomous systems differ with regard to the degree of system autonomy. The National Highway Traffic Safety Administration (NHTSA) defines five degrees of car autonomy which vary in the penetration of cars with Advanced Driver Assistance Systems (ADAS) and the extent to which a car is taken over by autonomous systems. Based on these levels, we conducted an online-questionnaire study (N = 336), in which we investigated how UA and UX factors, such as Perceived Ease of Use, Attitude Towards using the system, Perceived Behavioral Control, Behavioral Intention to use a system, Trust and Fun, differ with regard to the degree of autonomy in cars. We show that UA and UX are highest in levels of autonomy that already have been deployed in modern cars. More specifically, perceived control and fun decrease continuously with higher autonomy. Furthermore, our results indicate that pre-experience with ADAS and demographics, such as age and gender, have an influence on UA and UX.
To practitioner and researcher alike, consumer values play an important role in understanding behavior in the marketplace. This paper presents a model linking perceived product attributes to values.
Safe interaction with interactive systems in the car requires both hands to be placed on the steering wheel and eyes to be kept on the road. To allow safe text input in the vehicle, we propose the back of the steering wheel as space for interactive text input elements. In the effort to explore this space, we present two design alternatives for text input elements; one has two sliding sensors and the other has three buttons on each side of the wheel. In combination with a head up display and an adapted keyboard layout, these elements allow text input while driving with the eyes on the road an the hands on the wheel. In a first study with end users, we show the potential of the proposed text input approach for future vehicles.
The major categories of models of the past two decades are reviewed in order to pinpoint their strengths - and perhaps their weaknesses - in that framework. This review includes such models as McKnight & Adams' task analysis, Kidd & Laughery's early behavioral computer simulations, the linear control models (such as McRuer & Weir's), as well as some more recent concepts such as Naeaetaenen & Summala's, Wilde's and Fuller's risk coping models which already carry some cognitive weight. Having proposed my answers to these questions an attempt is made to formulate an alternative approach, based on production systems as developed by J. R. Anderson.
