eHMI for All -- Investigating the Effect of External Communication of Automated Vehicles on Pedestrians, Manual Drivers, and Cyclists in Virtual Reality

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.

💡 Research Summary

The paper addresses a critical gap in autonomous vehicle (AV) research: the lack of comparative studies on external Human‑Machine Interfaces (eHMIs) across multiple road‑user roles. While most prior work focuses on pedestrian‑AV interactions, this study evaluates a single, unified eHMI design—called the Slow‑Pulsing Light Band (SPLB)—with three distinct user perspectives: pedestrians, drivers of manually driven vehicles (MDVs), and cyclists. Using a within‑subjects virtual‑reality (VR) experiment, 40 participants experienced 18 scenarios each, covering three distraction conditions (no distraction, visual noise, and direct interference) and two eHMI states (on vs. off). The SPLB consists of cyan LEDs mounted on all four sides of the vehicle that pulse slowly whenever the AV intends to yield, providing a language‑independent visual cue.

The experimental design is a 2 (eHMI) × 3 (role) × 3 (distraction) repeated‑measures layout. Subjective outcomes include safety perception, trust, perceived usefulness, and NASA‑TLX mental demand; objective metrics capture reaction times, virtual collision risk, and distance to the vehicle. Statistical analysis via repeated‑measures ANOVA reveals strong main effects of the eHMI: across all roles, participants report higher safety, greater trust, higher usefulness, and lower mental demand when the SPLB is active. Distraction and role also show significant main effects—participants are generally more challenged under visual noise or interference, and pedestrians exhibit higher mental load than drivers or cyclists. Interaction effects are largely absent, except for perceived usability, where role and distraction modulate the eHMI benefit.

Behaviorally, the eHMI reduces average reaction time by 0.42 seconds and lowers simulated collision risk by 18 %. These findings suggest that a single, well‑designed visual eHMI can convey yielding intent effectively to a heterogeneous set of road users, supporting the notion that standardization is feasible without sacrificing performance.

The authors discuss several limitations. The VR environment, while highly immersive, cannot fully replicate physical risk or environmental variability of real streets. The participant pool is young, educated, and culturally homogeneous, limiting generalizability to older adults, children, or individuals with sensory impairments. Only one visual modality was tested; future work should compare SPLB with auditory, projected, or haptic cues and explore multimodal combinations. Long‑term habituation and learning effects were not examined.

In conclusion, the study provides robust empirical evidence that a unified external communication system can improve safety‑related perceptions and reduce cognitive load for pedestrians, drivers, and cyclists alike, even under distracting traffic conditions. This supports the push toward standardized eHMI solutions in forthcoming Level‑4/5 autonomous vehicles, while highlighting the need for real‑world validation, broader demographic sampling, and multimodal interface research.