Exploration of Factors Impacting the Successful Adoption of External Vehicle Interfaces

Reimer, Bryan · 2019 · ROSA P / New England University Transportation Center

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Summary

This research addresses the critical need to understand non-verbal communication between pedestrians and drivers, particularly in the context of emerging vehicle automation and external human-machine interfaces (eHMIs). As automated vehicles may lack traditional visual cues like eye contact, there is concern that new displays are necessary to convey vehicle intent. However, foundational questions regarding how pedestrians currently interpret driver attention and vehicle kinematics remained unanswered. The study aimed to determine if seeing a driver or their gaze is a critical signal for crossing decisions and to assess how automation might disrupt established interaction patterns. The researchers employed a multi-method approach combining crowdsourced experiments, naturalistic driving data, large-scale simulations, and immersive virtual reality (VR) studies. Initial experiments used high-resolution static imagery to test the ability of pedestrians to perceive a driver’s presence and gaze orientation under various lighting conditions and distances. Subsequent analyses utilized both simulated and real-world datasets to examine the relationship between vehicle trajectories, specifically time-to-arrival (TTA), and pedestrian gap acceptance. Finally, a VR study involving 22 subjects explored social cues by engineering 15 distinct vehicle trajectories, some adhering to social conventions and others subverting them, to observe how pedestrians responded to manipulated kinematic signals. The findings challenge the widely held belief that pedestrians rely on eye contact with drivers to make crossing decisions. Evidence showed that in over 90% of cases under representative lighting conditions, pedestrians cannot determine a driver’s gaze at 15 meters and cannot see the driver at all at 30 meters. Consequently, at common city speeds, more than 99% of pedestrians initiate crossing before they can visually confirm eye contact, relying instead on vehicle kinematics. The study confirmed that TTA is a key signal in crossing decisions, with pedestrians giving themselves less time when vehicles travel faster. Furthermore, the VR experiments demonstrated that pedestrians use vehicle kinematics to infer social intentions, not just physical state, and that these social cues can be engineered through trajectory manipulation. The significance of these results lies in their implications for the design of eHMIs for automated vehicles. The research suggests that automation systems must account for pedestrians’ tendency to overestimate TTA at higher speeds to maximize safety. Crucially, it warns that eHMIs could disrupt established kinematic responsiveness, as pedestrians may pause to read displays rather than reacting instinctively to vehicle movement. These insights have been shared with international working groups and published in peer-reviewed papers to enhance stakeholder understanding of pedestrian-vehicle interactions, guiding the development of interfaces that complement rather than hinder natural non-verbal communication.

Key finding

Over 90% of viewers could not determine an approaching driver's gaze at 15 m and could not see the driver at all at 30 m, so at 25 mph more than 99% of pedestrians would begin crossing before being able to see the driver.

Methodology

mixed_methods

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (9 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover success rosap 2 2026-05-23
archive success 1 2026-05-23
extract success cached 2 2026-06-10
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-23
promote success 1 2026-05-23
summarize success llm qwen3.6-27b-prismaquant summ-v5 3 2026-06-10
tag success vector_similarity 19 2026-06-11
verify partial 5 2026-06-10

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

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