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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.
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.
(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
Da erst wenige automatisierte Fahrzeuge auf öffentlichen Straßen in Betrieb sind, ist aktuell noch unklar, ob potenzielle Nutzer diese akzeptieren und ihnen vertrauen. Um diesen Fragen nachzugehen, wurde eine Feldstudie mit 24 (jeweils zwölf älteren und jüngeren) Teilnehmern durchgeführt, wobei ein automatisiertes Fahrzeug direkt mit einem von menschlicher Hand gesteuerten Gruppentaxi verglichen wurde. Benutzerakzeptanz und -erlebnis, Vertrauen sowie subjektives Zeitempfinden wurden sowohl vor als auch nach der Fahrt mit dem jeweiligen Transportmittel mit standardisierten Messverfahren und Interviews erfasst. Die Resultate zeigen, dass automatisierte Fahrzeuge in ähnlichem Ausmaß akzeptiert werden wie auch menschliche Fahrer, jedoch gerade jüngere Probanden aufgrund der geringeren Geschwindigkeit der neuen Technologie gegenüber noch skeptisch eingestellt sind. Für diese Zielgruppe wirkte sich jedoch eine Fahrt mit dem automatisierten Fahrzeug positiv auf das Vertrauen aus. Eine Berücksichtigung der gewonnenen Erkenntnisse ist für eine weitere Implementierung der Technologie zu empfehlen.
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.
Attentive user interfaces to improve multitasking and take-over performance in automated driving
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Teleoperation: the holy grail to solve problems of automated driving? Sure, but latency matters
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ATHENA: supporting UX of conditionally automated driving with natural language reliability displays
(2019)