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In the future, automated cars may feature external human–machine interfaces (eHMIs) to communicate relevant information to other road users. However, it is currently unknown where on the car the eHMI should be placed. In this study, 61 participants each viewed 36 animations of cars with eHMIs on either the roof, windscreen, grill, above the wheels, or a projection on the road. The eHMI showed ‘Waiting’ combined with a walking symbol 1.2 s before the car started to slow down, or ‘Driving’ while the car continued driving. Participants had to press and hold the spacebar when they felt it safe to cross. Results showed that, averaged over the period when the car approached and slowed down, the roof, windscreen, and grill eHMIs yielded the best performance (i.e., the highest spacebar press time). The projection and wheels eHMIs scored relatively poorly, yet still better than no eHMI. The wheels eHMI received a relatively high percentage of spacebar presses when the car appeared from a corner, a situation in which the roof, windscreen, and grill eHMIs were out of view. Eye-tracking analyses showed that the projection yielded dispersed eye movements, as participants scanned back and forth between the projection and the car. It is concluded that eHMIs should be presented on multiple sides of the car. A projection on the road is visually effortful for pedestrians, as it causes them to divide their attention between the projection and the car itself.

External Human–Machine Interfaces (eHMIs) enhance pedestrian safety in interactions with autonomous vehicles (AVs) by signaling crossing risk based on time-to-arrival (TTA), categorized as low, medium, or high. This study compared five eHMI configurations (single-level low, medium, high; two-level low-medium, medium-high) against a three-level (low-medium-high) configuration to assess their impact on pedestrians’ crossing decisions, mental workload (MW), and situation awareness (SA) in vehicle platoon scenarios under full and partial eHMI penetration. In a video-based experiment with 24 participants, crossing decisions were evaluated via temporal gap selection, MW via P300 event-related potentials in an auditory oddball task, and SA via the Situation Awareness Rating Technique. The three-level configuration outperformed single-level medium, single-level high, two-level low-medium, and two-level medium-high in gap acceptance, promoting safer decisions by rejecting smaller gaps and accepting larger ones, and exhibited lower MW than the two-level medium-high configuration under partial penetration. No SA differences were observed. Although the three-level configuration was generally appreciated, future research should optimize presentation to mitigate issues from rapid signal changes. Notably, the single-level low configuration showed comparable performance, suggesting a simpler alternative for real-world eHMI deployment.

While light-based external human–machine interfaces (eHMIs) on automated vehicles (AVs) are increasingly studied to mediate pedestrian–vehicle conflicts, gaps persist in understanding how specific dynamic features of the AV’s headlights influence pedestrians’ prediction of its yielding intention and their crossing behavior. This study systematically investigates the effects of dynamic elements of vehicle lighting—including animation patterns, animation speed, and light-emitting area—on pedestrians’ objective and subjective evaluations. A factorial design framework was employed, where participants viewed video simulations of an approaching AV displaying headlight designs combining multiple dynamic features. For different vehicle motion states, the vehicle–pedestrian distance was integrated as a variable to examine its interaction effect with lighting features. Objective measures of cueing effects were complemented by subjective ratings and user preference study via questionnaires. Results showed that there were more crossing behaviors of the pedestrian when presenting higher animation speed of dynamic light eHMIs. Animation pattern and light-emitting area does not play an important role in pedestrian decision-making, but proper design of these two features can evoke higher visual attention. When the vehicle–pedestrian distance is longer, the dynamic features of lighting will more affect people’s willingness to cross. The effects of light eHMIs seemed more significant for the AV travelling in constant speed. Our findings advance preliminary suggestions for selecting light-based eHMIs in the appropriate scenarios and can contribute actionable insights for designing intuitive, human-centric AV–pedestrian negotiation strategies.

Numerous studies have emerged on the external human–machine interface (eHMI) to facilitate the communication between automated vehicles (AVs) and other road users. However, it remains to be determined which eHMI modality and location are proper for the pedestrian–AV interaction. Therefore, a video-based, eye-tracking study was performed to investigate how pedestrians responded to AVs with eHMIs in different modalities (flashing text, smiley, light band, sweeping pedestrian icon, arrow, and light bar) and locations (grill, windshield, and roof). Moreover, the effects of pedestrian-related factors (e.g., gender, sensation-seeking level, and traffic accident involvement) were also included and evaluated. The dependent variables included pedestrians’ clarity-rating scores towards these eHMI concepts, road-crossing decision time, and gaze-based metrics (e.g., fixation counts, dwell time, and first fixation duration). The results showed that the text, icon, and arrow-based eHMIs resulted in the shortest decision time, highest clarity scores, and centralized visual attention. The light strip-based eHMIs yielded no significant decrease in decision time yet longer fixation time, indicating difficulties in comprehension of their meaning without learning. The eHMI location had no effect on pedestrians’ decision time but a substantial influence on their visual searching strategy, with a roof eHMI contradicting pedestrians’ inherent scanning pattern. These findings provide implications for the standardized design of future eHMIs.

In future traffic, automated vehicles may be equipped with external human-machine interfaces (eHMIs) that can communicate with pedestrians. Previous research suggests that, during first encounters, pedestrians regard text-based eHMIs as clearer than light-based eHMIs. However, in much of the previous research, pedestrians were asked to imagine crossing the road, and unable or not allowed to do so. We investigated the effects of eHMIs on participants’ crossing behavior. Twenty-four participants were immersed in a virtual urban environment using a head-mounted display coupled to a motion-tracking suit. We manipulated the approaching vehicles’ behavior (yielding, nonyielding) and eHMI type (None, Text, Front Brake Lights). Participants could cross the road whenever they felt safe enough to do so. The results showed that forward walking velocities, as recorded at the pelvis, were, on average, higher when an eHMI was present compared to no eHMI if the vehicle yielded. In nonyielding conditions, participants showed a slight forward motion and refrained from crossing. An analysis of participants’ thorax angle indicated rotation towards the approaching vehicles and subsequent rotation towards the crossing path. It is concluded that results obtained via a setup in which participants can cross the road are similar to results from survey studies, with eHMIs yielding a higher crossing intention compared to no eHMI. The motion suit allows investigating pedestrian behaviors related to bodily attention and hesitation.

Research on external human–machine interfaces (eHMIs) has recently become a major area of interest in the field of human factors research on automated driving. The broad variety of methodological approaches renders the current state of research inconclusive and comparisons between interface designs impossible. To date, there are no standardized test procedures to evaluate and compare different design variants of eHMIs with each other and with interactions without eHMIs. This article presents a standardized test procedure that enables the effective usability evaluation of eHMI design solutions. First, the test procedure provides a methodological approach to deduce relevant use cases for the evaluation of an eHMI. In addition, we define specific usability requirements that must be fulfilled by an eHMI to be effective, efficient, and satisfying. To prove whether an eHMI meets the defined requirements, we have developed a test protocol for the empirical evaluation of an eHMI with a participant study. The article elucidates underlying considerations and details of the test protocol that serves as framework to measure the behavior and subjective evaluations of non-automated road users when interacting with automated vehicles in an experimental setting. The standardized test procedure provides a useful framework for researchers and practitioners.

As automated driving system (ADS) technology is adopted in wheelchairs, clarity on the vehicle’s imminent path becomes essential for both users and pedestrians. For users, understanding the imminent path helps mitigate anxiety and facilitates real-time adjustments. For pedestrians, this insight aids in predicting their next move when near the wheelchair. This study introduces an on-ground projection-based shared eHMI approach for autonomous wheelchairs. By visualizing imminent motion intentions on the ground by integrating real and virtual elements, the approach quickly clarifies wheelchair behaviors for all parties, promoting proactive measures to reduce collision risks and ensure smooth wheelchair driving. To explore the practical application of the shared eHMI, a user interface was designed and incorporated into an autonomous wheelchair simulation platform. An observation-based pilot study was conducted with both experienced wheelchair users and pedestrians using structured questionnaires to assess the usability, user experience, and social acceptance of this interaction. The results indicate that the proposed shared eHMI offers clearer motion intentions display and appeal, emphasizing its potential contribution to the field. Future work should focus on improving visibility, practicality, safety, and trust in autonomous wheelchair interactions.

Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner.

External human–machine interfaces (eHMIs) have the potential to benefit AV–pedestrian interactions. The majority of studies investigating eHMIs have used relatively simple traffic environments, i.e., a single pedestrian crossing in front of a single eHMI on a one-lane straight road. While this approach has proved to be efficient in providing an initial understanding of how pedestrians respond to eHMIs, it over-simplifies interactions which will be substantially more complex in real-life circumstances. A process is illustrated in a small-scale study (N = 10) to rank different crossing scenarios by level of complexity. Traffic scenarios were first developed for varying traffic density, visual complexity of the road scene, road geometry, weather and visibility conditions, and presence of distractions. These factors have been previously shown to increase difficulty and riskiness of the crossing task. The scenarios were then tested in a motion-based, virtual reality environment. Pedestrians’ perceived workload and objective crossing behaviour were measured as indirect indicators of the level of complexity of the crossing scenario. Sense of presence and simulator sickness were also recorded as a measure of the ecological validity of the virtual environment. The results indicated that some crossing scenarios were more taxing for pedestrians than others, such as those with road geometries where traffic approached from multiple directions. Further, the presence scores showed that the virtual environments experienced were found to be realistic. This paper concludes by proposing a “complex” environment to test eHMIs under more challenging crossing circumstances.

External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question, we developed a Unity game in which the player drove a conventional car and the AVs were operating with eHMIs. We examined the effects of different eHMI designs—namely, textual, graphical, and anthropomorphic—on the driving behavior of a player in a gaming environment, and compared it to one with no eHMI. Participants (N = 18) had to follow a specified route, using the typical keys for PC games. They encountered AVs with an eHMI placed on the rear window. Five scenarios were simulated for the specified routes: school safety zone; traffic island; yellow traffic light; waiting for passengers; and an approaching e-scooter. All scenarios were repeated three times (a total of 15 sessions per participant), and the eHMI was randomly generated among the four options. The behavior was determined by observing the number of violations in combination with keystrokes, fixations, and saccades. Their subjective evaluations of the helpfulness of the eHMI and their feelings about future AVs revealed their attitudes. Results showed that a total of 45 violations occurred, the most frequent one being exceeding the speed limit in the school safety zones (37.8%) when the eHMI was textual, anthropomorphic, graphical, and when there was no eHMI, in decreasing order; the next was collisions (33.3%), when the eHMI was anthropomorphic, none, or graphical. The rest were ignoring the red light (13.3%), crossing the stop line (13.3%), and violation of the central line (2.2%). More violations occurred when the eHMI was set to anthropomorphic, followed by no eHMI, graphical, and textual eHMI. The helpfulness of the five scenarios scored high (5.611 to 6.389) on a seven-point Likert scale, and there was no significant difference for the scenarios. Participants felt more positive about the future of AVs after their gaming experience (p = 0.049). We conclude that gazing at unfamiliar and ambiguous information on eHMIs may cause a loss of driver attention and control. We propose an adaptive approach in terms of timing and distance depending on the behavior of other road users.