External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions
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Summary
This study investigates how external human-machine interfaces (eHMIs) on automated vehicles (AVs) influence pedestrians’ decisions to cross the street. As AVs lack human drivers, traditional non-verbal cues like eye contact disappear, potentially hindering safe and efficient interactions. The research aimed to determine if specific eHMIs could improve pedestrian safety (avoiding unsafe crossings) and efficiency (crossing when safe) by clearly communicating vehicle intentions. The researchers conducted a virtual reality experiment using a head-mounted display with 28 participants. The study employed a within-subject design manipulating four variables: vehicle size (small, medium, large), yielding behavior (yielding vs. nonyielding), eHMI type (baseline/no eHMI, front brake lights, Knightrider animation, smiley face, or text "WALK"), and eHMI timing (early, intermediate, or late relative to vehicle deceleration). Participants continuously pressed a button to indicate when they felt safe to cross. The experiment simulated an urban environment without zebra crossings to increase ambiguity, requiring pedestrians to rely on vehicle behavior and eHMIs. For nonyielding vehicles, eHMIs had no significant effect on perceived safety; however, larger vehicles resulted in lower "feel-safe" percentages. For yielding vehicles, all four eHMI types significantly increased the percentage of time participants felt safe to cross compared to the baseline condition. The timing of the eHMI also mattered: early and intermediate timing yielded higher feel-safe percentages than late timing. Notably, the text-based eHMI ("WALK") required no learning period, achieving high feel-safe percentages immediately. In contrast, the other three eHMIs (brake lights, Knightrider, smiley) showed a significant learning effect, with participants becoming more comfortable crossing only after repeated exposure. The study concludes that eHMIs enhance the efficiency of pedestrian-AV interactions by helping pedestrians recognize when a vehicle intends to yield. The textual display was identified as the least ambiguous and most immediately effective interface, while other visual cues required a learning phase. These findings support the development of AVs equipped with clear, direct communication systems to ensure safe integration into public road environments.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-25 |
| archive | success | openalex | — | — | 5 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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