A Preliminary Exploration to Enhance Appropriate Level of Trust in Autonomous Vehicle and Pedestrian Interaction

As autonomous vehicles (AVs) are increasingly piloted in urban environments, establishing appropriate levels of pedestrian trust is critical for ensuring both safety and traffic efficiency. This study examines the influence of external human-machine interfaces (eHMIs) on pedestrian trust during AV encounters at unsignalized crosswalks. A virtual reality (VR) experiment involving 50 participants was conducted to assess how variations in eHMI timing and vehicle behavior affect trust calibration. The experimental design included a between-subject factor comparing failure scenarios with immediate versus delayed eHMI display, and a within-subject factor encompassing seven vehicle behavior conditions. Trust ratings were collected after each scenario and analyzed using a linear mixed-effects model. Results show that the timing and consistency of eHMI signals significantly influence pedestrian trust. Notably, delayed eHMI messages led to more cautious trust responses in failure scenarios, while still maintaining high trust in successful yielding conditions. These findings suggest that strategically delaying eHMI communication may mitigate overtrust and support safer pedestrian-AV interactions. The study contributes preliminary evidence toward designing trust-calibrated AV communication systems.

Authors

Institutions

Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2025-07-24
DOI
https://doi.org/10.1177/10711813251358785
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Preliminary Exploration to Enhance Appropriate Level of Trust in Autonomous Vehicle and Pedestrian Interaction

Marufa Islam, Jinjuan She
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Human-Automation Interaction and Safety
article

A Preliminary Exploration to Enhance Appropriate Level of Trust in Autonomous Vehicle and Pedestrian Interaction

Marufa Islam, Jinjuan She
article en

Abstract

As autonomous vehicles (AVs) are increasingly piloted in urban environments, establishing appropriate levels of pedestrian trust is critical for ensuring both safety and traffic efficiency. This study examines the influence of external human-machine interfaces (eHMIs) on pedestrian trust during AV encounters at unsignalized crosswalks. A virtual reality (VR) experiment involving 50 participants was conducted to assess how variations in eHMI timing and vehicle behavior affect trust calibration. The experimental design included a between-subject factor comparing failure scenarios with immediate versus delayed eHMI display, and a within-subject factor encompassing seven vehicle behavior conditions. Trust ratings were collected after each scenario and analyzed using a linear mixed-effects model. Results show that the timing and consistency of eHMI signals significantly influence pedestrian trust. Notably, delayed eHMI messages led to more cautious trust responses in failure scenarios, while still maintaining high trust in successful yielding conditions. These findings suggest that strategically delaying eHMI communication may mitigate overtrust and support safer pedestrian-AV interactions. The study contributes preliminary evidence toward designing trust-calibrated AV communication systems.

Proceedings of the Human Factors and Ergonomics Society Annual MeetingVol. 69(1)
Health First (US), Miami University (US)
University of Miami
Openalex Percentile: Top 79%
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.