With automated vehicles (AVs), the absence of a human operator could necessitate external Human-Machine Interfaces (eHMIs) to communicate with other road users. Existing research primarily focuses on pedestrian-AV interactions, with limited attention given to other road users, such as cyclists and drivers of manually driven vehicles. So far, no studies have compared the effects of eHMIs across these three road user roles. Therefore, we conducted a within-subjects virtual reality experiment (N=40), evaluating the subjective and objective impact of an eHMI communicating the AV’s intention to pedestrians, cyclists, and drivers under various levels of distraction (no distraction, visual noise, interference). eHMIs positively influenced safety perceptions, trust, perceived usefulness, and mental demand across all roles. While distraction and road user roles showed significant main effects, interaction effects were only observed in perceived usability. Thus, a unified eHMI design is effective, facilitating the standardization and broader adoption of eHMIs in diverse traffic.

As autonomous vehicles enter public spaces, external human–machine interfaces are proposed to support communication with external road users. A decade of research has produced hundreds of studies and reviews, yet it remains unclear whether the field is converging on shared principles or diverging across approaches. We present a multi-dimensional analysis of 620 publications, complemented by industry deployments and regulatory documents, to track research evolution and identify convergence. The analysis reveals several field-level patterns. First, convergence on a safety-first core: simple visual cues that clarify intent. Second, sustained divergence in necessity and implementation. Third, a progressive filtering funnel: broad exploration in research and concepts narrows in deployment and is codified by regulation into a minimal set of permitted signals. These insights point to a shift in emphasis for future work, from producing new prototypes toward consolidating evidence, clarifying points of contention, and developing frameworks that can adapt across contexts.

External Human-Machine Interfaces (eHMIs) have been proposed to enhance communication between automated vehicles (AVs) and pedestrians, with growing interest in multi-modal designs such as audio-visual eHMIs. Just as poor lighting can impair visual cues, a loud background noise may mask the auditory stimuli. However, its effects within these systems have not been examined, and little is known about how pedestrians — particularly Deaf and Hard-of-Hearing (DHH) people — perceive different types of auditory stimuli. We conducted a virtual reality study (Hearing N=25, DHH N=11) to examine the effects of background noise (quiet and loud) on auditory stimuli (baseline, bell, speech) within an audio-visual eHMI. Results revealed that: (1) Crossing experiences of DHH pedestrians significantly differ from Hearing pedestrians. (2) Loud background noise adversely affects pedestrians’ crossing experiences. (3) Providing an additional auditory eHMI (bell/speech) improves crossing experiences. We outlined four practical implications for future eHMI design and research.

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

Numerous driving simulator systems are available and are continuing to be developed.However, we believe many simulator offerings are built around what is technically possible rather than what is useful to the researchers that might use such systems.This points to a critical need to understand what makes a driving simulator practical and effective for automotive interface design researchers.To remedy this shortcoming, we conducted video interviews with 15 industry and academic researchers engaged in automotive interface design research.We transcribed and performed thematic analysis on the data collected to better understand the different ways that researchers are using driving simulators, and what challenges they still face.We identified needs across three broad dimensions including: (1) Participant Experience, (2) Research Needs, and (3) Operationalization Requirements.By categorizing these needs, we aim to inform the development of future simulation tools that are more accessible to researchers from diverse backgrounds. CCS Concepts• Human-centered computing → Systems and tools for interaction design.

Figure 1: We conducted a Virtual Reality driving simulation study to explore interactions between a human driver and an autonomous vehicle at traffic intersections in a pseudo-naturalistic setting to understand the influence of contingent driving behaviors in different contexts (two of four scenarios shown above).

Amidst the replication crisis, it is increasingly clear that we need to understand contextual factors that drive participant behavior, because those factors influence the applicability of study findings more broadly. For AutoUI, as we conduct interaction studies involving drivers, pedestrians, and other traffic participants, it is useful to characterize the traffic contexts that human participants are familiar with, because their prior experiences with traffic are likely to influence their behaviors within the context of a controlled study.

In this review, we analyze the current state of the art of computational models for in-vehicle User Interface (UI) design. Driver distraction, often caused by drivers performing Non Driving Related Tasks (NDRTs), is a major contributor to vehicle crashes. Accordingly, in-vehicle User Interfaces (UIs) must be evaluated for their distraction potential. Computational models are a promising solution to automate this evaluation, but are not yet widely used, limiting their real-world impact. We systematically review the existing literature on computational models for NDRTs to analyze why current approaches have not yet found their way into practice. We found that while many models are intended for UI evaluation, they focus on small and isolated phenomena that are disconnected from the needs of automotive UI designers. In addition, very few approaches make predictions detailed enough to inform current design processes. Our analysis of the state of the art, the identified research gaps, and the formulated research potentials can guide researchers and practitioners toward computational models that improve the automotive User Interface (UI) design process.

External Human-Machine Interfaces (eHMIs) have been evaluated to facilitate interactions between Automated Vehicles (AVs) and pedestrians. Most eHMIs are, however, visual/ light-based solutions, and multi-modal eHMIs have received little attention to date. We ran an experimental video study (N = 29) to systematically understand the effect on pedestrian’s willingness to cross the road and user preferences of a light-based eHMI (light bar on the bumper) and two sound-based eHMIs (bell sound and droning sound), and combinations thereof. We found no objective change in pedestrians’ willingness to cross the road based on the nature of eHMI, although people expressed different subjective preferences for the different ways an eHMI may communicate, and sometimes even strong dislike for multi-modal eHMIs. This shows that the modality of the evaluated eHMI concepts had relatively little impact on their effectiveness. Consequently, this lays an important groundwork for accessibility considerations of future eHMIs, and points towards the insight that provisions can be made for taking user preferences into account without compromising effectiveness.

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.

Driving a car can be difficult when it comes to distractions caused by operating the in-vehicle infotainment system (IVIS). In-car passengers often help with performing IVIS-related tasks. However, an IVIS is often not designed with a focus on task collaboration. In this article, we focus on how to design in-car systems with the goal to support collaboration between a driver and a front-seat passenger. Based on infotainment-oriented tasks, we initially explore five key collaborative control concepts by means of an IVIS which differ from each other in terms of the number of available IVIS screens (one or two), access to menus (restricted and unrestricted), and the nature of performing tasks in parallel or one after the other. Results from a simulator study with N = 16 pairs show significant effects of the concepts on social collaboration in terms of perceived social connectedness (measured with sub-dimensions connectedness, affiliation, belongingness, companionship), team performance (coordination effectiveness and team cohesion), and fairness. We found that especially a dedicated passenger IVIS screen empowers front-seat passengers, reduces power dynamics, supports fairness, and minimizes driver distraction (caused by interacting passengers). We discuss the implications of these findings and posit recommendations to design future IVIS in passenger cars with improved driver-passenger collaboration by explicitly designing for balanced power roles, situational awareness, active communication, and a balance between drivers’ privacy and trust toward the passenger. Additionally, we outline a systematic overview of future work to explore the research field of driver-passenger collaboration in more breadth and depth.

With the gradual transition towards assisted and automated driving, the car will transform into a more social environment where passengers and drivers engage in Non-Driving-Related Activities (NDRA). To support collaboration among occupants in future vehicles, research suggests interactive systems controlled by several users at once. In this paper, we explore five concepts for the collaborative performance of NDRA with the use-case of music playlist creation. While prior work investigated the effect on social connectedness, we expand insights towards team performance and fairness. Results from a mixed-subject experiment (N=27) show that the concepts have major consequences on team performance and fairness. Certain concepts can promote or hinder coordination effectiveness and, in turn, impact intra-vehicular collaboration. Our observations also indicate that fairness is key to fostering social collaboration in AVs, while it does not naturally define a high team performance. Subsequently, we provide recommendations to guide future designs of collaborative NDRAs in vehicles.

Current research on external Human-Machine Interfaces (eHMIs) in facilitating interactions between automated vehicles (AVs) and pedestrians have largely focused on one-to-one encounters. In order for eHMIs to be viable in reality, they need to be scalable, i.e., facilitate interaction with more than one pedestrian with clarity and unambiguity. We conducted a virtual-reality-based empirical study to evaluate four eHMI designs with two pedestrians. Results show that even in this minimum criteria of scalability, traditional eHMI designs struggle to communicate effectively whom the AV intends to yield to. Road-projection-based eHMIs show promise in clarifying the specific yielding intention of an AV, although it may still not be an ideal solution. The findings point towards the need to consider the element of scalability early in the design process, and potentially the need to reconsider the current paradigm of eHMI design.

External human-machine interfaces (eHMIs) support automated vehicles (AVs) in interacting with vulnerable road users such as pedestrians. While related work investigated various eHMIs concepts, these concepts communicate their message in one go at a single point in time. There are no empirical insights yet whether distance-dependent multi-step information that provides additional context as the vehicle approaches a pedestrian can increase the user experience. We conducted a video-based study (N = 24) with an eHMI concept that offers pedestrians information about the vehicle’s intent without providing any further context information, and compared it with two novel eHMI concepts that provide additional information when approaching the pedestrian. Results show that additional distance-based information on eHMIs for yielding vehicles enhances pedestrians’ comprehension of the vehicle’s intention and increases their willingness to cross. This insight posits the importance of distance-dependent information in the development of eHMIs to enhance the usability, acceptance, and safety of AVs.

In this paper, we investigate the effect of an external human-machine interface (eHMI) and a conspicuous external vehicle appearance due to visible sensors on pedestrian interactions with automated vehicles (AVs). Recent research shows that AVs may need to explicitly communicate with the environment due to the absence of a driver. Furthermore, in interaction situations, an AV that looks different and conspicuous owing to an extensive sensor system may potentially lead to hesitation stemming from mistrust in automation. Thus, we evaluated in a virtual reality study how pedestrian attitude, the presence/absence of an eHMI, and a conspicuous sensor system affect their willingness to cross the road. Results recommend the use of an eHMI. A conspicuous appearance of automated-driving capability had no effect for the sample as a whole, although it led to more efficient crossing decisions for those with a more negative attitude towards AVs. Our findings contribute towards the effective design of future AV interfaces.

In this paper, we report user preferences regarding color and animation patterns to support the interaction between Automated Vehicles (AVs) and pedestrians through an external Human-Machine-Interface (eHMI). Existing concepts of eHMI differ -- among other things -- in their use of colors or animations to express an AV's yielding intention. In the absence of empirical research, there is a knowledge gap regarding which color and animation leads to highest usability and preferences in traffic negotiation situations. We conducted an online survey (N=400) to investigate the comprehensibility of a light band eHMI with a combination of 5 color and 3 animation patterns for a yielding AV. Results show that cyan is considered a neutral color for communicating a yielding intention. Additionally, a uniformly flashing or pulsing animation is preferred compared to any pattern that animates sideways. These insights can contribute in the future design and standardization of eHMIs.

In road-crossing situations involving negotiation with approaching vehicles, pedestrians need to take into account the behavior of the car before making a decision. To investigate the kind of information about the car that pedestrians seek, and the places where do they look for it, we conducted an eye-tracking study with 26 participants and analyzed the fixation behavior when interacting with a manually-driven vehicle that approached while slowing and displaying a yielding behavior. Results show that a clear pattern of gaze behavior exists for pedestrians in looking at a vehicle during road-crossing situations as a function of the vehicle's distance. When the car is far away, pedestrians look at the environment or the road space ahead of the car. With the approach, the gaze gradually shifts to the windshield of the car. We conclude by discussing the implications of this insight in the user-centered-design of optimal external Human-Machine-Interfaces for automated vehicles.

This paper presents a study that aimed to identify the importance of eye contact and gestures between pedestrians and drivers. A video-based observation and coding was undertaken to categorize the road-crossing and communication behavior of pedestrians and drivers in busy traffic situations where efficient negotiation is necessary. The evidence in the study suggests that eye contact does not play a major role in manual driving, that explicit communication is rare to non-existent, and that motion patterns and behaviors of vehicles play a more significant role for pedestrians in efficient traffic negotiations.