Human autonomy is a core concept that helps explain the acceptance of and interaction with computer systems and AI technology. However, autonomy is often vaguely defined and conflated with related constructs. This paper disentangles autonomy by integrating the dualistic nature of positive and negative liberty from the perspective of political philosophy. Using an online vignette study with N=194 participants, we show that positive and negative liberty act as correlated but distinct dimensions of the autonomy foundation. While negative liberty predicts the sense of agency, positive liberty is a key dimension for people’s willingness to use technology. We argue that this dualistic stand - positive liberty as the freedom to pursue authentic goals, and negative liberty as the freedom from external constraints - offers a valuable and actionable perspective on human autonomy that can inform future system design and better answer the ambivalent question “how much autonomy is enough”?

In high-stakes decision-making, the acceptance of AI recommendations depends not only on system accuracy but also on how decisions are explained. While prior work on explainable AI has largely focused on transparency and interpretability, less attention has been paid to the persuasive dimension of explanations. To address this gap, we investigate how rhetorical strategies drawn from Cialdini’s persuasion theory, when embedded in natural language explanations generated by large language models (LLMs), influence user compliance and their ability to recognize persuasive intent. We conducted a controlled survey study with 129 participants in two application domains—finance and healthcare—where participants evaluated both a baseline and a persuasive explanation for an AI-generated decision across favorable and unfavorable outcomes. In a complementary task, participants rated ten short explanations on perceived persuasiveness and factual strength, enabling us to measure awareness of persuasive intent. Our results show that persuasive explanations significantly increased compliance in the healthcare scenario (p <.001), whereas baseline explanations were more effective in finance (p <.001), regardless of whether the AI decision was positive or negative. A notable proportion of participants rated explanations containing at least one of Cialdini’s persuasion techniques as highly persuasive, yet simultaneously judged them to be factually weaker. Importantly, we found no statistically significant evidence that participants’ ability to recognize persuasive intent influenced compliance. These findings highlight the dual role of persuasive explanations: they can enhance foster compliance in sensitive contexts such as healthcare but risk undermining trust in domains like finance. For HCI and IUI, our study underscores that explanations are not neutral vessels of information: their rhetorical form substantially shapes how users perceive and engage with AI-assisted decision-making. Designers of explainable AI systems should therefore carefully balance transparency and persuasion when developing interfaces for high-stakes applications.

Automated vehicles (AVs) reached technological maturity and will soon arrive on streets as traffic participants.Human traffic participants such as drivers, pedestrians, or cyclists will be increasingly confronted with the presence of AVs within their environment, not necessarily knowing or understanding what to expect and how to interact with them.Although AVs are designed to act safely, effective interaction in mixed traffic scenarios will depend on successful communication, interaction, or even negotiation beyond static rules and regulations.Prosocial behavior, such as yielding one's right of way, will be needed to resolve unclear traffic situations or foster traffic flow.However, what are the characteristics of such prosocial behavior, and how to measure this not only for automated vehicles

How do drivers perceive risk?Understanding what situations and factors cause drivers to perceive situations as critical can improve our understanding of road user behavior and inform automated driving technology.To investigate the factors that shape drivers' risk perception, we conducted an eye-tracking study with 27 participants who watched dashcam videos and continuously rated the perceived risk of various driving situations.Using the resulting dataset, we developed a computer vision-based machine learning approach that generates explainable predictions of perceived risk from video and eye-tracking data.Our SHAP analysis reveals that the proximity of objects and number of cars in a scene are the most significant contributors to perceived risk.Most interestingly, while people tend to sample similar objects in critical situations, their risk

In VR locomotion, walking performance can vary significantly depending on the locomotion method. In our study, participants completed in total 12 walking trials in virtual environments using either arm-swing or joystick method. Arm-swing interface maintains stable trajectory control from the outset, while joystick locomotion shows significant improvement with repeated trials, indicating a strong learning effect across different scenes. These findings suggest that physical locomotion method may be preferable for tasks requiring immediate stability, whereas artificial locomotion may excel in scenarios allowing for practice and demanding high precision.

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents’ intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

Promoting sustainable mobility requires technological innovation and changes in individual travel behavior. Using the Theory of Planned Behavior, we examined how attitudes, norms, and perceived control shape the willingness to adopt alternatives to car use. We designed 38 future commuting scenarios, each of which isolated a single dimension across three mobility concepts: public transportation, cycling, and shared automated vehicles. In an online survey (N = 168), participants rated their willingness to switch modes and pay more. To deepen our understanding, we conducted follow-up interviews (N = 10), exploring their everyday mobility practices and their likes and dislikes regarding the practicality of the future scenarios. Our findings show that features linked to instrumental attitudes and control beliefs elicit stronger intentions than affective cues, ecological appeals were less persuasive. We argue that effective behavior change depends on linking motivational factors to the realities of everyday mobility contexts.

We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating scientific progress and promoting an inclusive scientific community. However, there is little documentation of these aspects within the automotive user research community. To address this, we report two studies that identify (1) community perspectives on motivators and barriers to data sharing, and (2) how openness and transparency have changed in papers published at AutomotiveUI over the past 5 years. We show that while open science is valued by the community and openness and transparency have improved, overall compliance is low. The most common barriers are legal constraints and confidentiality concerns. Although research published at AutomotiveUI relies more on quantitative methods than research published at CHI, openness and transparency are not as well established. Based on our findings, we provide suggestions for improving openness and transparency, arguing that the motivators for open science must outweigh the barriers. All supporting materials are freely available at: https://osf.io/zdpek/

Situation Awareness (SA) is one of the core concepts describing drivers’ interaction with vehicles, and the lack of SA has contributed to multiple incidents with automated systems. Despite existing definitions and measurements, little is known about what constitutes the concept of situations from users’ perspective, i.e., do they have a similar or different understanding of situation dynamics? Therefore, we conducted a video-based experiment where participants had to mark the onset of new situations from their perspective, provide a continuous criticality rating, and justify their decisions in a post-test interview. Our results indicate that the understanding of situations, their complexity, and their duration is quite diverse between people and independent of properties such as age, gender, or driving experience, while partly being influenced by the road type. Additionally, we found correlations between subjective situation durations, criticality ratings, and algorithm output, which can be exploited by future applications and experiments.

The Wizard-of-Oz method has been widely used recently as it allows mimicking automated vehicles with relatively few resources. In some studies, it is challenging to ensure that the wizard remains fully hidden from participants, despite this being a crucial aspect of such experiments. To determine whether participants’ awareness of the wizard influences the outcomes of these studies, we conducted an experiment investigating participants’ crossing behavior and subjective perception of a remote-controlled automated vehicle. Participants were exposed to two conditions: in one, they solely focused on a simulated vehicle driving autonomously; in the other, they observed a wizard with a remote control and were instructed to imagine the car was automated. Results, based on scales for user experience, acceptance, and trust, as well as crossing behavior, indicate similar results. However, participants’ knowledge of the wizard necessitates careful interpretation when system errors are simulated. We conclude with recommendations for future Wizard-of-Oz experiments.

Research on cycling safety has recently gained the attention of the HCI community. While there have been multiple proposals for automated driving features on bikes, we are unaware of a project that systematically aims to translate and evaluate driver assistance systems from the automotive to the bike domain to promote cycling safety in traffic. Thus, we implemented an adaptive cruise control and a lane-keeping/centering system with hard- and software on a motion-based bicycle simulator and investigated their potential in a virtual reality experiment. Based on performance measurements and subjective ratings, results showed significant improvements in technology acceptance, subjective workload, and driving performance regarding the cruise control. In contrast, the lane-centering and lane-keeping features were rated significantly worse than the baseline without such assistance. The paper concludes with a critical reflection on automated driving features for bicycles.

Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings.

Studies across various task types suggest that collaboration between humans and AI leads to improved and more satisfying outcomes. However, the effects of prolonged use of AI on users’ skills and perceptions remain unclear. This study involved 12 participants using an AI-based assistant in a multitasking balancing game over five days. Our findings indicate that AI assistance improved participants’ performance, even for the condition that was not supported by AI, showing no deskilling effect. Additionally, participants experienced significantly lower cognitive load (measured via ocular pupil diameter) in the AI-supported condition. This suggests that AI-assisted training can enhance multitasking motor skills in a less stressful and cognitively demanding manner. We also found a positive correlation between users’ understanding of the AI assistant and its acceptance, highlighting the importance of transparency and effectively communicating the capabilities of AI applications.

With the increasing development of Virtual Reality, locomotion has become an essential component of interaction in VR. Currently, various locomotion technologies have been developed to provide users with a natural walking experience in virtual environments. However, the multiple walking techniques impact users’ walking experience in different ways. Simulator sickness is a common issue in VR experiences. Since different walking methods may influence simulator sickness differently, we conducted a user study to evaluate simulator sickness in walking with three relevant walking methods: real walking, arm-swing, and omnidirectional treadmill, and the results indicated that these three walking methods caused different levels of simulator sickness, and people perceived stronger sickness when they walked on the omnidirectional treadmill.

Automated vehicles (AVs) are expected to encounter various ambiguous space-sharing conflicts in urban traffic. Bottleneck scenarios, where one of the parts needs to resolve the conflict by yielding priority to the other, could be utilized as a representative ambiguous scenario to understand human behavior in experimental settings. We conducted a controlled field experiment with a Wizard of Oz automated car in a bottleneck scenario. 24 participants attended the study by driving their own cars. They made yielding, or priority-taking decisions based on implicit and explicit locomotion cues on AV realized with an external display. Results indicate that acceleration and deceleration cues affected participants’ driving choices and their perception regarding the social behavior of AV, which further serve as ecological validation of related simulation studies.

Appropriate monitoring and successfully intervening when automation fails is one of the most critical issues in level 2 automated driving, since drivers suffer from low situation awareness when using such systems. To counter, we present a gamified in-vehicle interface based on ideas from previous work, where drivers have to support the vehicle by pointing at other traffic objects in the environment. We hypothesized that this system could help drivers in the monitoring task, maintain their situation awareness, and result in lower crash rates. We implemented a prototype of this system and evaluated it in a lab study with N=20 participants. The results indicate that participants were looking more intensively at lead vehicles and performed stronger braking actions. However, there was no measurable benefit on situation awareness and intervention performance in critical situations. We conclude by discussing differences to related experiments and present future ideas.

Attention management systems seek to minimize disruption by intelligently timing interruptions and helping users navigate multiple tasks and activities. While there is a solid theoretical basis and rich history in HCI research for attention management, little progress has been made regarding their practical implementation and deployment. Building sophisticated attention management systems requires a great variety of sensors, task- and user models, and multiple devices while considering the complexity of user context and human behavior. Novel AI technologies, such as generative systems, reinforcement learning, and large language models, open new possibilities to create intelligent, practical, and user-centered attention management systems. This proposed workshop aims to bring together researchers and practitioners from diverse backgrounds to discuss and formulate a research agenda to advance attention management systems using novel AI tools to manage and mitigate interruptions from computing systems effectively.

Notifications and interruptions have shown to significantly impede task performance while causing stress. Attention management systems aim at mitigating these negative effects, for example, by delaying interruptions to task boundaries or times of low mental load. However, while the theoretical benefits of such an approach are well-documented, it is quite unclear how holding back information from users is accepted, especially in times of the “always-on-mentality”. Thus, we conducted an online vignette experiment with N=163 participants, who were presented hypothetical private and work-related scenarios where interruptions are delayed by attention management systems. Participants rated how long they would allow particular interruptions to be delayed, as well as which data collection methods a system could use to perform these decisions. Our results show that interruption management is desired by potential users, provided they feel in control. We conclude with recommendations for the design of attention management systems.

Research on cyclists‘ safety and comfort is a growing topic. Existing works address support systems with novel interaction concepts such as augmented reality but also the design and evaluation of high-fidelity bicycle simulators. Since the field is still in its exploratory phase, there have been few attempts to systematically provide guidance for conducting experiments. For example, there is no consensus on the choice of representative driving scenarios, the proper choice of different bicycle simulators, and measurement standards to systematically compare the results of different studies. With this workshop, we want the community to gather and discuss a roadmap for the future of HCI bicycle research so that these issues can be overcome.

Functional specificity describes the degree to which operators can successfully calibrate their trust toward different subsystems of a machine. Only a few works have addressed this issue in the context of automated vehicles. Previous studies suggest that drivers have issues distinguishing between different subsystems, which leads to low functional specificity. To counter, this article presents a prototypical design where different in-vehicle subsystems are portrayed by independent conversational agents. The concept was evaluated in a user study where participants had to supervise a level 2 automated vehicle while reading and communicating with the conversational agents in the car. It was hypothesized that a clear differentiation between subsystems could allow drivers to better calibrate their trust. However, our results, based on subjective trust scales, monitoring, and driving behavior, cannot confirm this assumption. In contrast, functional specificity was high among participants of the study, and they based their situational and general trust ratings mainly on the perceptions of the driving automation system. Still, the experiment contributes to issues of trust and monitoring and concludes with a list of relevant findings to support trust calibration in supervisory control situations.

Studies on imminent Take-Over Requests (TORs) in automated driving have mainly addressed safety aspects rather than user experience (UX). In this study, we investigated the fulfillment of user needs during SAE L3 driving on a test track. Participants engaged in non-driving related tasks (NDRTs; using a smartphone or the auditory modality) had to respond to critical TORs to prevent an accident. Our results, based on qualitative methods, show that participants expect L3 vehicles to be safe and confirmed this assessment after the test track experience. Furthermore, participants preferred NDRTs using the auditory modality over the smartphone to maintain situation awareness. Our study indicates that drivers may behave responsibly in L3 vehicles, provided they are supported with user interfaces that fulfill their psychological needs.

Recently, researchers have proposed to develop automated self-balancing functions for bicycles to increase road safety and convenience. However, no study has investigated how self-balancing bicycles are perceived by potential users in a natural urban environment. Therefore, we conducted a field study using a modified “parent-child tandem” in which both the front and back seat passengers could share control of the riding dynamics. An experimenter in the back seat acted as an automated system, and participants in the front seat experienced the ride while responding to text messages on their smartphones. Based on interviews, video observation, and eye-tracking data, the results highlight potential use cases for self-balancing bicycles and uncover that trust and multitasking freedom can lead to similar problems as in automated cars.

Research in human-robot collaboration explores aspects of using interaction modalities and their effect on human perception. Particular attention is paid to intent communication, which is essential for successful interaction and collaboration. This work investigates the effect of using audio, visual, and haptic feedback on intent communication in a human-robot collaboration task where the collaborators do not share a direct line of sight. A user study was conducted in virtual reality with 20 participants. Qualitative and quantitative feedback was collected from all participants. When compared with a baseline of no feedback given to the participants, results show that using visual feedback had a significant impact on task efficiency, user experience, and cognitive load. Audio feedback was slightly less impactful, while haptic feedback had a divisive effect. Multimodal feedback combining the three modalities showed the highest impact compared to the individual modalities, leading to the highest task efficiency and user experience, and the lowest cognitive load.

Bicycle simulators are becoming an increasingly used research tool. However, due to the complex cycling dynamics, these simulators have issues of simulator sickness and perceived realism. A potential method to address these issues could be providing a motion-based tilting function. Some bicycle simulators with tilt functionality have already been presented but still lack a systematic evaluation. In this work, we present a motion-based bicycle simulator without centrifugal force simulation and the results from a user study that compared different tilt modes. N=31 participants completed a study in virtual reality with a strong and a weak tilt mode, as well as a baseline condition without movement. We discovered that weak tilting could significantly improve the cycling realism without decreasing cycling performance and simulator sickness. Furthermore, our research suggests that there is a sweet spot for a tilting function, which facilitates a balance between presence/immersion and simulator sickness.

Automated Vehicles (AVs) are expected to radically disrupt our mobility. Whereas much is speculated about how AVs will actually be implemented in the future, we argue that their advent should be taken as an opportunity to enhance all people’s mobility and improve their lives. Thus, it is important to focus on both the environment and the needs of target groups that have not been sufficiently considered in the past. In this paper, we present the findings from a qualitative study (N=11) of public attitude on hypothetical implementation scenarios for AVs. Our results indicate that people are aware of the benefits of shared mobility for the environment and society, and are generally open to using it. However, 1) emotional factors mitigate this openness and 2) security concerns were expressed by female participants. We recommend that identified concerns must be addressed to allow AVs fully exploiting their benefits for society and environment.

Trust is important in determining how drivers interact with automated vehicles. Overtrust has contributed to fatal accidents; and distrust can hinder successful adoption of this technology. However, existing studies on trust are often hard to compare, given the complexity of the construct and the absence of standardized measures. Further, existing trust scales often do not consider its multi-dimensionality. Another challenge is that driving is strongly context- and situation-dependent. We present the Situational Trust Scale for Automated Driving, a short questionnaire to assess different aspects of situational trust, based on the trust model proposed by Hoff and Bashir. We evaluated the scale using an online study in the US and Germany (N=303), where participants faced different videos of an automated vehicle. Results confirm the existence of situational factors as components of trust, and support the scale being a valid measure of situational trust in this automated driving context.

Recent research indicates that transparent information on the behavior of automated vehicles positively affects trust, but how such feedback should be composed and if user trust influences the amount of desired feedback is relatively unexplored. Consequently, we conducted an interview study with (N=56) participants, who were presented different videos of an automated vehicle from the ego-perspective. Subjects rated their trust in the vehicle in these situations and could arbitrarily select objects in the driving environment that should be included in augmented reality feedback systems, so that they are able to trust the vehicle and understand its actions. The results show an inverse correlation between situational trust and participants’ desire for feedback and further reveal reasons why certain objects should be included in feedback systems. The study also highlights the need for more adaptive in-vehicle interfaces for trust calibration and outlines necessary steps for automatically generating feedback in the future.

The intentions of an automated vehicle are hard to spot in the absence of eye contact with a driver or other established means of communication. External car displays have been proposed as a solution, but what if they malfunction or display misleading information? How will this influence pedestrians' trust in the vehicle? To investigate these questions, we conducted a between-subjects study in Virtual Reality (N = 18) in which one group was exposed to erroneous displays. Our results show that participants already started with a very high degree of trust. Incorrectly communicated information led to a strong decline in trust and perceived safety, but both recovered very quickly. This was also reflected in participants' road crossing behavior. We found that malfunctions of an external car display motivate users to ignore it and thereby aggravate the effects of overtrust. Therefore, we argue that the design of external communication should avoid misleading information and at the same time prevent the development of overtrust by design.

In the domain of automated driving, numerous (technological) problems were solved in recent years, but still many limitations are around that could eventually prevent the deployment of automated driving systems (ADS) beyond SAE level 3. A remote operating fallback authority might be a promising solution. In order for teleoperation to function reliably and universal, it will make use of existing infrastructure, such as cellular networks. Unfortunately, cellular networks might suffer from variable performance. In this work, we investigate the effects of latency on task performance and perceived workload for different driving scenarios. Results from a simulator study (N=28) suggest that latency has negative influence on driving performance and subjective factors and led to a decreased confidence in Teleoperated Driving during the study. A latency of about 300 ms already led to a deteriorated driving performance, whereas variable latency did not consequently deteriorate driving performance.

With increasing automation, vehicles could soon become "mobile offices" but traditional user interfaces (UIs) for office work are not optimized for this domain. We hypothesize that productive work will only be feasible in SAE level 3 automated vehicles if UIs are adapted to (A) the operational design domain, and (B) driver-workers' capabilities. Consequently, we studied adapted interfaces for a typical office task (text-comprehension) by varying display modality (heads-up reading vs. auditory listening), as well as UI behavior in conjunction with take-over situations (attention-awareness vs. no attention-awareness). Self-ratings, physiological indicators, and objective performance measures in a driving simulator study (N = 32) allowed to derive implications for a mobile workspace automated vehicle. Results highlight that heads-up displays promote sequential multi-tasking and thereby reduce workload and improve productivity in comparison to auditory displays, which were still more attractive to users. Attention-awareness led to reduced stress but later driving reactions, consequently requiring further investigations.

In the evolution of technical systems, freedom from error and early adoption plays a major role for market success and to maintain competitiveness. In the case of automated driving, we see that faulty systems are put into operation and users trust these systems, often without any restrictions. Trust and use are often associated with users' experience of the driver-vehicle interfaces and interior design. In this work, we present the results of our investigations on factors that influence the perception of automated driving. In a simulator study, N=48 participants had to drive a SAE level 2 vehicle with either perfect or faulty driving function. As a secondary activity, participants had to solve tasks on an infotainment system with varying aesthetics and usability (2x2). Results reveal that the interaction of conditions significantly influences trust and UX of the vehicle system. Our conclusion is that all aspects of vehicle design cumulate to system and trust perception.

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.

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.

As automated vehicles increase on public roads, research on communication with vulnerable road users (VRUs) becomes increasingly important. Recently, numerous solutions to tackle this problem have been presented. However, it is not clear whether such concepts fit the needs and requirements of future users. The aim of this work is to identify the requirements and expectations of users after real-life exposure with an automated vehicle. We conducted a field study (N=32) in a small town where an automated vehicle is in regular operation and collected both subjective (surveys, semi-structured interviews) and objective (video analysis) data. Results suggest that VRUs prefer rather simple forms of communication derived from well-established concepts like traffic signs, horns or indicators. Furthermore, we could identify important scenarios that have yet not been addressed, and our findings are in particular useful for the provision of automated vehicles in shared spaces.

Drivers' ability to engage in non-driving related tasks (NDRTs) is a promise of automated driving and office work an important use-case therein. We claim that potentially negative effects on road safety need to be compensated by adaptive in-vehicle interfaces that support NDRTs by design. In this paper, we present the conception and evaluation of a novel dual-task interface that is based on findings from both automotive research and office ergonomics. The steering wheel prototype aims at enabling productivity while retaining or even improving safety in Take-Over situations. In a driving simulator study with N=22 participants, we tested the prototype in two variations, haptic vs. touchscreen keyboard design, against the baseline “notebook on the lap”. Results show significant improvements regarding gaze reaction, typing performance, and subjective ratings of the haptic as compared to the touch keyboard. Most promisingly, Take-Over reaction time decreased by 40 percent when using the haptic prototype instead of the conventional notebook. Based on our findings, we recommend the use of adaptive input devices to assist the driver and prevent mode confusion. We further suggest avoiding the use of tablets in L3 driving - even when integrated into the steering wheel - in order to meet safety requirements.

The prevalence of Automated Driving Systems (ADS) is expected to open up many possibilities for different user groups with individual needs and challenges. Former secondary/tertiary tasks can become primary tasks, and driving with all its interactions and responsibilities steps back or disappears at all. At higher levels of AD it is expected that the elderly could maintain or regain individual mobility, thus, play a major role for future markets. To understand individual mindsets concerning technology acceptance and user needs we conducted an explorative interview study (N=27). In a simulated automated driving environment, driving experience over time was compared across three age groups (elderly people >65, younger adults <30, younger adults <30 with age simulation suite), utilizing the STAM model for content analysis. Results of the age-comparison indicate no major differences in the general technology acceptance, however, fine-grained analysis revealed interesting differences in participants' perceptions concerning UX design requirements.

A major promise of automated vehicles is to render it possible for drivers to engage in nondriving related tasks, a setting where the execution pattern will switch from concurrent to sequential multitasking. To allow drivers to safely and efficiently switch between multiple activities (including vehicle control in case of Take-Over situations), we postulate that future vehicles should incorporate capabilities of attentive user interfaces, that precisely plan the timing of interruptions based on driver availability. We propose an attention aware system that issues Take-Over Requests (1) at emerging task boundaries and (2) directly on consumer devices such as smartphones or tablets. Results of a driving simulator study (N=18), where we evaluated objective, physiological, and subjective measurements, confirm our assumption: attention aware Take-Over Requests have the potential to reduce stress, increase Take-Over performance, and can further raise user acceptance/trust. Consequently, we emphasize to implement attentive user interfaces in future vehicles.

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.

A prerequisite to foster proliferation of automated driving is common system acceptance. However, different users groups (novice, enthusiasts) decline automation, which could be, in turn, problematic for a successful market launch. We see a feasible solution in the combination of the advantages of manual (autonomy) and automated (increased safety) driving. Hence, we've developed the Hotzenplotz interface, combining possibility-driven design with psychological user needs. A simulator study (N=30) was carried-out to assess user experience with subjective criteria (Need Scale, PANAS/-X, HEMA, AttrakDiff) and quantitative measures (driving behavior, HR/HRV) in different conditions. Our results confirm that pure AD is significantly less able to satisfy user needs compared to manual driving and make people feeling bored/out of control. In contrast, the Hotzenplotz interface has proven to reduce the negative effects of AD. Our implication is that drivers should be provided with different control options to secure acceptance and avoid deskilling.

Safety systems in automated vehicles use surround sensors to perceive their local environment. In contrast to radar sensors, laser scanners provide precise spatial information, which can be used to identify critical traffic situations and trigger reversible or irreversible safety systems. As a consequence, already small errors in sensor data measurements could lead to severe accidents. The performance of surround sensors depends on the ambient atmosphere and weather condition. It is known that rain and fog have negative influence and leads to signal degradation. In winter time or cold climate, the ambient air temperature requires less moisture to attain high relative humidity or to become saturated. Therefore, the hot exhaust gases of vehicles are condensed instantly and can be seen as a distinct mass of fog. To the best of our knowledge, nobody has investigated effects of exhaust gases at low temperatures on sensor quality so far. Based on static and dynamic tests at different temperatures, we have shown that exhaust gases are visible for laser scanners and lead to degraded performance. The results can be used to assign the priorities of sensors in sensor data fusion processes or to develop novel filter algorithms for low temperature situations.

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 semi-structured 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.

Technological advances will soon make it possible for automated systems (such as vehicles or search and rescue drones) to take over tasks that have been performed by humans. Still, it will be humans that interact with these systems - relying on the system ('s decisions) will require trust in the robot/machine and its algorithms. Trust research has a long history. One dimension of trust, ethical or morally acceptable decisions, has not received much attention so far. Humans are continuously faced with ethical decisions, reached based on a personal value system and intuition. In order for people to be able to trust a system, it must have widely accepted ethical capabilities. Although some studies indicate that people prefer utilitarian decisions in critical situations, e.g. when a decision requires to favor one person over another, this approach would violate laws and international human rights as individuals must not be ranked or classified by personal characteristics. One solution to this dilemma would be to make decisions by chance - but what about acceptance by system users? To find out if randomized decisions are accepted by humans in morally ambiguous situations, we conducted an online survey where subjects had to rate their personal attitudes toward decisions of moral algorithms in different scenarios. Our results (n=330) show that, despite slightly more respondents state preferring decisions based on ethical rules, randomization is perceived to be most just and morally right and thus may drive decisions in case other objective parameters equate.

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.

Both amount as well as dynamicity of content to be displayed in a car increases steadily, forcing manufacturer to change over to customizable screens integrated in dashboard and center console instead of dozens to hundreds of individual control signals. In addition, new requirements such as Internet access in the car or web services accessible while driving invalidates rudimentary display formats. Traditional forms of interaction such as buttons or knobs are unsuitable to respond to dynamic content shown on digital screens, requesting new mechanisms for distraction-free yet effective user (driver) input. We pick up this problem by introducing a novel sensing device allowing for natural, contactless, and eyes-free operation by relating finger movements in the area of the gearshift to screen coordinates. To assess quality features of this interface two research questions were formulated, (i) that the application of such a device would allow for natural, intuitive mouse pointer control in a similar manner than traditional forms of input and (ii) that the interface is insusceptible to varying workload conditions of the driver. Results from experimentation have revealed that, with respect to the first hypothesis, proximity sensing in a two-dimensional plane is a viable approach to directly control a mouse cursor on a screen integrated into the dashboard. A generally accepted conclusion on the assumption that the index of performance of the interface does not change with varying workload (hypothesis ii) cannot be drawn. To simulate different conditions of workload a dual task signal-response setting was used.