(1) Background: Primary driving tasks are increasingly being handled by vehicle automation so that support for non-driving related tasks (NDRTs) is becoming more and more important. In SAE L3 automation, vehicles can require the driver-passenger to take over driving controls, though. Interfaces for NDRTs must therefore guarantee safe operation and should also support productive work.
(2) Method: We conducted a within-subjects driving simulator study (N=53) comparing Heads-Up Displays (HUDs) and Auditory Speech Displays (ASDs) for productive NDRT engagement. In this article, we assess the NDRT displays’ effectiveness by evaluating eye-tracking measures and setting them into relation to workload measures, self-ratings, and NDRT/take-over performance.
(3) Results: Our data highlights substantially higher gaze dispersion but more extensive glances on the road center in the auditory condition than the HUD condition during automated driving. We further observed potentially safety-critical glance deviations from the road during take-overs after a HUD was used. These differences are reflected in self-ratings, workload indicators and take-over reaction times, but not in driving performance.
(4) Conclusion: NDRT interfaces can influence visual attention even beyond their usage during automated driving. In particular, the HUD has resulted in safety-critical glances during manual driving after take-overs. We found this impacted workload and productivity but not driving performance
While virtual reality (VR) interfaces have been researched extensively over the last decades, studies on their application in vehicles have only recently advanced. In this paper, we systematically review 12 years of VR research in the context of automated driving (AD), from 2009 to 2020. Due to the multitude of possibilities for studies with regard to VR technology, at present, the pool of findings is heterogeneous and non-transparent. We investigated N = 176 scientific papers of relevant journals and conferences with the goal to analyze the status quo of existing VR studies in AD, and to classify the related literature into application areas. We provide insights into the utilization of VR technology which is applicable at specific level of vehicle automation and for different users (drivers, passengers, pedestrians) and tasks. Results show that most studies focused on designing automotive experiences in VR, safety aspects, and vulnerable road users. Trust, simulator and motion sickness, and external human-machine interfaces (eHMIs) also marked a significant portion of the published papers, however a wide range of different parameters was investigated by researchers. Finally, we discuss a set of open challenges, and give recommendation for future research in automated driving at the VR side of the reality-virtuality continuum.
There is a growing body of research in the field of interaction between automated vehicles and other road users in their vicinity. To facilitate such interactions, researchers and designers have explored designs, and this line of work has yielded several concepts of external Human-Machine Interfaces (eHMI) for vehicles. Literature and media review reveals that the description of interfaces is often lacking in fidelity or details of their functionalities in specific situations, which makes it challenging to understand the originating concepts. There is also a lack of a universal understanding of the various dimensions of a communication interface, which has impeded a consistent and coherent addressal of the different aspects of the functionalities of such interface concepts. In this paper, we present a unified taxonomy that allowsa systematic comparison of the eHMI across 18 dimensions, covering their physical characteristics and communication aspects from the perspective of human factors and human-machine interaction. We analyzed and coded 70eHMI concepts according to this taxonomy to portray the state of the art and highlight the relative maturity of different contributions. The results point to a number of unexplored research areas that could inspire future work. Additionally, we believe that our proposed taxonomy can serve as a checklist for user interface designers and researchers when developing their interfaces.
Artificial 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.
There is a growing body of research in the field of interaction between drivers/passengers and automated vehicles using augmented reality (AR) technology. Furthering the advancements and availability of AR, the number of use cases in and around vehicles rises. Our literature review reveals that in the past, AR research focussed on increasing road safety and displaying navigational aids, however, more recent research explores the support of immersive (non-)driving related activities, and finally enhance driving and passenger experiences, as well as assist other road users through external human-machine interfaces (HMIs). AR may also be the enabling technology to increase trust and acceptance in automated vehicles through explainable artificial intelligence (AI), and therefore help on the shift from manual to automated driving. We organized a workshop addressing AR in automotive human-computer interaction (HCI) design, and identified a number of challenges including human factors issues that need to be tackled, as well as opportunities and practical usages of AR in future mobility. We believe that our status-quo literature analysis and future-oriented workshop results can serve as a research agenda for user interface designers and researchers when developing automotive AR interfaces.
Cyclists frequently face numerous hazards on the road. Often those hazards are posed by motorised vehicles. Advanced support systems that alert cyclists to potential dangers could enhance their safety. However, research in this area, particularly regarding hazard notifications for cyclists, remains sparse. This work assesses bi-modal early hazard notification concepts (combining visual cues with either auditory or tactile feedback) provided at head level (smart glasses with speakers, tactile headband). They are detailing the nature of the hazard, its direction relative to the cyclist, and the timing of exposure. This work investigates cyclists' preference and perception of the proposed concepts for two hazardous situations originating from interactions with vehicles: ‘dooring’, the hazard of a potential collision with an opening door of a parked vehicle (evaluated through a test track study, N = 32) and ‘being overtaken’ which poses the hazard of being cut off or hit by the overtaking vehicle (assessed in a bicycle simulator study, N = 21). The study involved comparisons of supported and unsupported rides, focusing on their impact on usability, intuitiveness, workload, and perceived safety. Our findings reveal varied preferences for the supporting feedback modality, with 56% favouring visual-auditory and 31% visual-tactile. The participants rated user experience, intuitiveness and perceived safety for the use of both concepts quite high. Further, the workload for assisted rides was rated as equally low as for unassisted rides.
Es wurden bereits einige Lösungen vorgestellt, um die Kommunikation zwischen automatisierten Fahrzeugen und ungeschützten Verkehrsteilnehmern (sog. „Vulnerable Road Users“, VRUs) zu unterstützen. Noch ist jedoch unklar, ob diese Systeme den Anforderungen zukünftiger Benutzer auch gerecht werden. Ziel dieser Arbeit war es, Benutzeranforderungen von VRUs nach direktem Kontakt mit einem automatisierten Fahrzeug zu erfassen. Hierfür wurde eine Feldstudie mit 32 Teilnehmern durchgeführt. Die Resultate, welche sowohl auf subjektiven (Fragebögen, Interviews) als auch objektiven (Videoanalyse) Daten basieren, legen nahe, dass ungeschützte Verkehrsteilnehmer einfache und bekannte Konzepte (beispielsweise Ampelsysteme oder Hupen) zur Kommunikation bevorzugen. Des Weiteren wurden diverse Problemszenarien identifiziert, welche für eine Bereitstellung von automatisierten Fahrzeugen in „Shared Spaces“ von besonderer Bedeutung sind.
A Bermuda Triangle?: A Review of Method Application and Triangulation in User Experience Evaluation
(2018)
WeCARe: Workshop on Inclusive Communication between Automated Vehicles and Vulnerable Road Users
(2021)
Towards Personalized 3D Augmented Reality Windshield Displays in the Context of Automated Driving
(2022)
As vehicle automation advances, drivers of automated vehicles become more disengaged from the primary driving task. Windshield displays provide a large screen space supporting drivers in non-driving related activities. This article presents user preferences as well as task and safety issues for 3D augmented reality windshield displays in automated driving. Participants of a user study (n = 24) customized two modes of content presentation (multiple content-specific windows vs. one main window), and could freely adjust visual parameters for these content windows using a simulated “ideal” windshield display in a virtual reality driving simulator. We found that user preferences differ with respect to contextual aspects. Additionally, using one main content window resulted in better task performance and lower take-over times, but the subjective user experience was higher for the multi-window user interface. These findings help automotive interface designers to improve experiences in automated vehicles.
Context-Aware Profiles
(2006)
Content presentation on 3D augmented reality windshield displays in the context of automated driving
(2022)
The spread of automated vehicles (AVs) is expected to disrupt our mobility behavior. Currently, a male bias is prevalent in the technology industry in general, and in the automotive industry in particular, mainly focusing on white men. This leads to an under-representation of groups of people with other social, physiological, and psychological characteristics. The advent of automated driving (AD) should be taken as an opportunity to mitigate this bias and consider a diverse variety of people within the development process. We conducted a qualitative, exploratory study to investigate how shared automated vehicles (SAVs) should be designed from a pluralistic perspective considering a holistic viewpoint on the whole passenger journey by including booking, pick-up, and drop-off points. Both, men and women, emphasized the importance of SAVs being flexible and clean, whereas security issues were mentioned exclusively by our female participants. While proposing different potential solutions to mitigate security matters, we discuss them through the lens of the feminist HCI framework.
As a precursor to future public transportation, automated shuttle buses can already be experienced in some test regions, but the general public still has reservations and may not yet be ready for this change. For example, the fact that such vehicles might operate independently (without a human driver) creates a barrier of uncertainty and mistrust among people. In this work, we aim to identify and classify the prevailing reservations and propose solutions. We followed the User Centered Design (UCD) process to design concepts that are specifically tailored to the needs of future public transport users. After related work analysis, on-site research, and pre-studies, two main studies were conducted specifically to address communication in the exterior (n = 24) and interior/service design (n = 21). For both studies, we applied a mixed-methods approach combining quantitative and qualitative measures. Our results indicate that, in general, existing ways of communication in the exterior are insufficient to meet future needs. The two visualization concepts for external communication developed in this work were rated (significantly) better in most dimensions of the User Experience Questionnaire (UEQ), when compared to the baseline condition with no additional visualization. Furthermore, preferences among the study participants towards simple, highly visible, and well-known lighting concepts could be observed. As for the interior, the results show that participants rated attractiveness highly for the two design concepts (closer, further in the future) as compared to current, state-of-the-art solutions (automated buses currently in operation). For the “near future” concept, the pragmatic quality dominated, while in the other (the “far future”) concept the hedonic quality was in the foreground. From the results, design recommendations in different categories were derived, which reflect the general openness of the public towards new technologies and interior approaches, but also point out the importance for privacy and designated personal spaces inside an (automated) shuttle bus. Some of the results do not strictly apply to automated shuttle buses, and can serve as valuable suggestions for improving conventional shuttle buses.
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.