Anna-Katharina Frison

Automated Driving System, Male, or Female Driver: Who'd You Prefer? Comparative Analysis of Passengers' Mental Conditions, Emotional States & Qualitative Feedback

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.

A Novel Approach for Researching Crossing Behavior and Risk Acceptance: The Pedestrian Simulator

To integrate unpredictable human behavior in the assessment of active and passive pedestrian safety systems, we introduce a virtual reality (VR)-based pedestrian simulation system. The device uses the Xsens Motion Capture platform and can be used without additional infrastructure. To show the systems applicability for pedestrian behavior studies, we conducted a pilot study evaluating the degree of realism such a system can achieve in a typical unregulated pedestrian crossing scenario. Six participants had to estimate vehicle speeds and distances in four scenarios with varying gaps between vehicles. First results indicate an acceptable level of realism so that the device can be used for further user studies addressing pedestrian behavior, pedestrian interaction with (automated) vehicles, risk assessment and investigation of the pre-crash phase without the risk of injuries.

Towards a Personalized Trust Model for Highly Automated Driving

User acceptance of automated vehicles (and dependent dimensions such as road safety, frequency of use or level of recommendation) is said to be highly dependent on the operator’s individual trust in this technology. As a consequence, the development of driving functions and future driver-vehicle interfaces should allow for appropriate trust calibration. To better understand trust and the effect of mis-calibration on the way to a personalized trust model, we propose a set of trust-related research questions derived from related work and our own user studies. Based on preliminary investigation, we recommend examining 1) differences in users and subgroups of users, 2) different levels of trust based on situation or context, 3) methods for quantifying trust in naturalistic driving studies, and 4) definitions for an established/approved trust model and the individual calibration of the model with regard to driving behavior and automotive user interfaces. The final outcome should be a multidimensional trust model that fits the individual passenger/driver by dynamically adapting driving mode and UI representation/feedback.

Triangulation in UX Studies: Learning from Experience

While the consideration of User Experience (UX) has become embedded in research and design processes, UX evaluation remains a challenging and strongly discussed area for both researchers in academia and practitioners in industry. A variety of different evaluation methods have been developed or adapted from related fields, building on identified methodology gaps. Although the importance of mixed methods and data-driven approaches to get well-founded study results of interactive systems has been emphasized numerous times, there is a lack of evolved understandings and recommendations of when and in which ways to combine different methods, theories, and data related to the UX of interactive systems. The workshop aims to gather experiences of user studies from UX professionals and academics to contribute to the knowledge of mixed methods, theories, and data in UX evaluation. We will discuss individual experiences, best practices, risks and gaps, and reveal correlations among triangulation strategies.

Workaholistic: on balancing typing- and handover-performance in automated driving

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.

User Interfaces for Public Transport Vehicles: Future Opportunities and Challenges

Mobility is transcending towards flexible sharing, combined transportation modes, increased vehicle automation and digital customer services. User experience and acceptance are highly important criteria for the success of such novel concepts, and consequently their human interface has to be designed with creativity and responsibility. This workshop addresses this need by providing a holistic frame for ideation and discussion of user interface concepts for public transport vehicles. The expected outcome of the workshop is a set of opportunities, design concepts and challenges. These could be the input for a research agenda for the field.

Do moral robots always fail? Investigating human attitudes towards ethical decisions of automated systems

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.