"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational PathologyArtificial intelligence (AI)-based decision support systems hold promise for enhancing diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration can introduce and amplify cognitive biases, like confirmation bias caused by false confirmation when erroneous human opinions are reinforced by inaccurate AI output. This bias may increase under time pressure, a ubiquitous factor in routine pathology, as it strains practitioners' cognitive resources. We quantified confirmation bias triggered by AI-induced false confirmation and examined the role of time constraints in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results suggest that AI integration fuels confirmation bias, evidenced by a statistically significant positive linear-mixed-effects model coefficient linking AI recommendations mirroring flawed human judgment and alignment with system advice. Conversely, time pressure appeared to weaken this relationship. These findings highlight potential risks of AI in healthcare and aim to support the safe integration of clinical decision support systems.2025EREmely Rosbach et al.Technische Hochschule IngolstadtExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityCHI
The Significance of the Bystander Effect on Personal Responsibility in Critical Situations in Shared Automated VehiclesShared Automated Vehicles (SAVs) present a promising solution for future urban mobility. However, SAVs will reach the limits of their capabilities in some edge cases. Similar to personal AVs, passengers in SAVs might be utilized for this purpose, thus, methods for interaction between passengers and the automation or a teleoperator need to be explored. This study investigates whether the presence of other passengers leads to more passive behavior in critical situations (bystander effect). The results did not show significant differences in the participants' behavior depending on whether the ride was experienced alone or with other passengers. However, the qualitative data indicate that the presence of other passengers can trigger psychological processes that promote a bystander effect. The findings emphasize the importance of considering group effects in the context of SAVs. By understanding these dynamics, we aim to help design SAVs to promote safer and more inclusive future transport systems.2024BEBengt Escher et al.Automated Driving Interface & Takeover DesignTeleoperated DrivingV2X (Vehicle-to-Everything) Communication DesignAutoUI
Investigating Hazard Notifications for Cyclists in Mixed Reality: A Comparative Analysis with a Test Track StudyOne way to improve road safety for cyclists is the development of hazard notification systems. Instead of in field experiments, such systems could be tested in safe and more controlled simulated environments; however, their validity needs verification. We evaluated the validity of mixed reality (MR) simulation for bicycle support systems notifying of dooring hazards. In a mixed-design study (N=43) with environment type(MR/test track) as within and hazard notifications (with/without) as between factor, comparing subjective and objective measures across environments. In conclusion, MR simulation is absolutely valid for user experience and perceived safety and relatively valid for workload, standard deviation of lateral position, and speed. However, MR simulation was not valid for lateral distance, as participants cycled more in the center of the street than on the test track, perhaps to avoid simulator sickness. Thus, we conclude that MR simulation is valuable for studying bicycle safety.2023TSTamara von Sawitzky et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsV2X (Vehicle-to-Everything) Communication DesignAutoUI
Development of a Perceived Security Scale for Shared Automated Vehicles (PSSAVS) and its Validation in Colombia and GermanyPerceived security is crucial for the widespread adoption of shared automated vehicles (SAVs) and shuttle buses. However, there is currently no validated instrument to measure perceived security in this context, and little research has been done to determine the factors that contribute to perceived security. We propose the Perceived Security Scale for Shared Automated Vehicles (PSSAV), a questionnaire that assesses various aspects of perceived security in SAVs. The scale was evaluated using an exploratory, data-driven approach in a pilot study with 60 German participants, and a main study with 114 German and 101 Colombian participants experiencing a positive or negative ride in an automated shuttle bus (between-subjects design) presented as videos in an online study. The results suggest that trust, privacy, and control are key factors that influence security in the context of SAVs. The PSSAV questionnaire is reliable and sensitive to manipulation, indicating its construct validity.2023MSMartina Schuß et al.Automated Driving Interface & Takeover DesignMotion Sickness & Passenger ExperienceAutoUI
Communication of Uncertainty Information in Cooperative, Automated Driving: A Comparative Study of Different ModalitiesCooperation between drivers and automated vehicles requires transparent communication of the automation's current status. This can be achieved by communicating confidence or certainty in its current perception or decision. We evaluate different sensory modalities for communicating information about how safely an automated driving system can perform the driving task in critical traffic situations to a driver who is present as a cooperator. We aimed to improve communication between the driving system and the human driver, ultimately increasing the overall driving experience and performance. In a virtual reality driving simulation study with 34 participants, we presented confidence information across three modalities: visual, auditory, and vibrotactile, compared to a baseline condition. Our results indicate that communicating automation uncertainty through the auditory and vibrotactile modalities improved user experience, trust in automation, and perceived safety. At the same time, interactions with the Non-Driving Related Task were reduced by communicating confidence information in critical driving situations.2023JPJakob Peintner et al.Automated Driving Interface & Takeover DesignIn-Vehicle Haptic, Audio & Multimodal FeedbackAutoUI
Multimodal Error Correction for Speech-to-Text in a Mobile Office Automated Vehicle: Results From a Remote StudyFuture users of automated vehicles will demand the ability to perform diverse and extensive non-driving related tasks. However, prevailing restrictions in the car require new interaction concepts to enable productive office work. Intelligent voice-based interfaces may be a solution to facilitate productivity while at the same time keeping the ``driver in the loop'' and thereby maintaining safety. In this work, we investigated the repair problem of productive speech-to-text input in a highly automated vehicle. We examined the user experience of selecting/navigating to an incorrectly recognized word using only speech, pointing and clicking on a touchpad, and using mid-air hand gestures. Results indicate that hand gestures (condition VaG) have high hedonic quality but are not considered viable for error correction in productive text input. On the other hand, the unimodal (Voice-only baseline) and touchpad-based point-and-click (VaT) approaches to error correction were rated equally well in the hypothesized ``mobile office'' automated vehicle. The utilized remote study execution methodology proved to be a useful intermediary tool between pure online surveys and on-site studies for qualitative research during a pandemic but suffered from a lack of fidelity and options for objective usability and safety evaluation.2022CSClemens Schartmüller et al.Automated Driving Interface & Takeover DesignVoice User Interface (VUI) DesignHuman-LLM CollaborationIUI
Hazard Notifications for Cyclists: Comparison of Awareness Message Modalities in a Mixed Reality StudyCycling is an environmentally friendly means of transport with growing popularity. However, there is still potential for more road safety in the future. We argue that by making assistance systems available to cyclists, accidents could be prevented. In this paper, we focus on potential accidents caused by vehicle doors opening in a cyclist’s path of travel, which can lead to serious injuries to the cyclist. Using a mixed-methods approach, we investigated how messages informing about a potentially opening door ahead are perceived and understood regarding usability and intuitiveness in a bicycle simulator study (N=24). We investigated how visual messages, visual messages and auditory icons, and visual and voice messages on a head-mounted device are subjectively perceived compared to the baseline condition (no messages). We also assessed our participants’ attitudes toward using such systems and mixed reality simulations for bicycle safety research in general. Our results show that participants preferred visual messages and auditory cues and that they found these types of notifications more enjoyable than visual messages alone. Furthermore, the results suggest that such a system would be used while cycling. Participants agreed that mixed reality simulations are suitable for testing and evaluating novel support systems in the first step but confirmed that final real-world testing on the road is mandatory.2022TSTamara von Sawitzky et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsMicromobility (E-bike, E-scooter) InteractionPedestrian & Cyclist SafetyIUI
Let’s Share a Ride into the Future: A Qualitative Study Comparing Potential Implementation Scenarios of Automated Vehicles.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.2021MSMartina Schuß et al.Technische Hochschule Ingolstadt, Johannes Kepler UniversitätAutomated Driving Interface & Takeover DesignTeleoperated DrivingRidesharing PlatformsCHI
Novel Human-Machine Interfaces for the Management of User-Vehicle Transitions in Automated Driving For automated vehicles operating at SAE Level 4 capability, control could feasibly be passed from machine to human and vice versa -regardless of whether minimal risk condition exists as a fallback solution. We propose two Human-Machine Interfaces (HMIs) to assist in the management of these transitions: 1) A ‘Responsibility Panel’ providing the necessary feedback for a user to understand who must undertake different driving related activities (look, brake, throttle, steer) and who might be liable if a fault arises (user or car company); 2) A ‘Readiness to Drive’ testing HMI that only allows a human to retake control when a certain level of competency is demonstrated. Future work should evaluate the effectiveness of our HMIs.2019GBGary Burnett et al.Automated Driving Interface & Takeover DesignAutoUI
How Should Automated Vehicles Interact with Pedestrians? A Comparative Analysis of Interaction Concepts in Virtual RealityAutomated vehicles (AVs) introduce a new challenge to human-computer interaction (HCI): pedestrians are no longer able to communicate with human drivers. Hence, new HCI designs need to fill this gap. This work presents the implementation and comparison of different interaction concepts in virtual reality (VR). They were derived after an analysis of 28 works from research and industry, which were classified into five groups according to their complexity and the type of communication. We implemented one concept per group for a within-subject experiment in VR. For each concept, we varied if the AV is going to stop and how early it starts to activate its display. We observed effects on safety, trust, and user experience. A good concept displays information on the street, uses unambiguous signals (e.g., green lights) and has high visibility. Additional feedback, such as continuously showing the recognized pedestrian's location, seem to be unnecessary and may irritate.2019ALAndreas Löcken et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsSocial & Collaborative VRAutoUI
Why Do You Like To Drive Automated? A Context-Dependent Analysis of Highly Automated Driving to Elaborate Requirements for Intelligent User InterfacesTechnology 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.2019AFAnna-Katharina Frison et al.Automated Driving Interface & Takeover DesignAI-Assisted Decision-Making & AutomationIUI
S(C)ENTINEL - Monitoring Automated Vehicles with Olfactory Reliability DisplaysOverreliance 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.2019PWPhilipp Wintersberger et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Uncertainty VisualizationIUI
In UX We Trust: Investigation of Aesthetics and Usability of Driver-Vehicle Interfaces and Their Impact on the Perception of Automated DrivingIn 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.2019AFAnna-Katharina Frison et al.Technische Hochschule Ingolstadt & Johannes Kepler UniversityAutomated Driving Interface & Takeover DesignIn-Vehicle Haptic, Audio & Multimodal FeedbackAI-Assisted Decision-Making & AutomationCHI
Teleoperation: The Holy Grail to Solve Problems of Automated Driving? Sure, but Latency MattersIn 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.2019SNStefan Neumeier et al.Teleoperated DrivingAutoUI
Text Comprehension: Heads-Up vs. Auditory Displays - Implications for a Productive Work Environment in SAE Level 3 Automated VehiclesWith 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.2019CSClemens Schartmüller et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Voice User Interface (VUI) DesignAutoUI
The Real T(h)OR: Evaluation of Emergency Take-Over on a Test TrackTake-Over Requests are one of the most prominent topics in automated driving research and have recently been addressed by numerous publications. However, most results were solely obtained in simulated driving environments. Thus, it is important to investigate whether or not these results can be validated in user studies with real vehicles. We conducted an experiment on a test track, where a conditionally automated vehicle was simulated using a driving robot. Participants engaged in Non-Driving Related Tasks were interrupted by Take-Over Requests and had to avoid hitting a real obstacle. This video gives a first insight into our setting, which allows evaluation of imminent handover situations on a test track.2019AFAnna-Katharina Frison et al.Automated Driving Interface & Takeover DesignAutoUI
Who is Generation A? Investigating the Experience of Automated Driving for Different Age GroupsThe 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.2018AFAnna-Katharina Frison et al.Automated Driving Interface & Takeover DesignMotion Sickness & Passenger ExperienceAutoUI
Let Me Finish before I Take Over: Towards Attention Aware Device Integration in Highly Automated VehiclesA major promise of automated vehicles is to render it possible for drivers to engage in non-driving 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.2018PWPhilipp Wintersberger et al.Automated Driving Interface & Takeover DesignNotification & Interruption ManagementAutoUI
Interacting with Autonomous Vehicles: Learning from other DomainsThe 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.2018AMAlexander Meschtscherjakov et al.University of SalzburgAutomated Driving Interface & Takeover DesignAI Ethics, Fairness & AccountabilityHuman-Robot Collaboration (HRC)CHI