The Formation of Trust in Autonomous Vehicles after Interacting with Robotaxis on Public Roads

2025-09-30

AI Summary

This study investigates the dynamic evolution of pedestrian trust in Level 4 autonomous vehicles (AVs)—specifically robotaxis—at unsignalized urban intersections, addressing a critical gap in real-world human-AV interaction research. Method: Employing a naturalistic field experiment—the first of its kind in a commercial robotaxi deployment zone—we conducted repeated cross-sectional observations of pedestrian–AV interactions. Multidimensional assessment integrated the Pedestrian Behavior Questionnaire (PBQ), Perceived Robotaxi Features questionnaire (PRQF), AV Trust Scale, and Personal Innovativeness Scale (PIS). Contribution/Results: Participants exhibited significant increases in trust over time. Trust evolution correlated positively with PRQF’s interaction dimension and PBQ subscales measuring proactive behaviors and error perception. Findings underscore the pivotal roles of individual differences (e.g., innovativeness) and contextual interaction experiences in shaping trust—providing empirical foundations for designing trustworthy human–AV shared driving systems.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

This study investigates how pedestrian trust, receptivity, and behavior evolve during interactions with Level-4 autonomous vehicles (AVs) at uncontrolled urban intersections in a naturalistic setting. While public acceptance is critical for AV adoption, most prior studies relied on simplified simulations or field tests. We conducted a real-world experiment in a commercial Robotaxi operation zone, where 33 participants repeatedly crossed an uncontrolled intersection with frequent Level-4 Robotaxi traffic. Participants completed the Pedestrian Behavior Questionnaire (PBQ), Pedestrian Receptivity Questionnaire for Fully AVs (PRQF), pre- and post-experiment Trust in AVs Scale, and Personal Innovativeness Scale (PIS). Results showed that trust in AVs significantly increased post-experiment, with the increase positively associated with the Interaction component of PRQF. Additionally, both the Positive and Error subscales of the PBQ significantly influenced trust change. This study reveals how trust forms in real-world pedestrian-AV encounters, offering insights beyond lab-based research by accounting for population heterogeneity.

Problem

• How pedestrian trust evolves during interactions with autonomous vehicles
• How pedestrian receptivity changes in real-world AV encounters
• How pedestrian behavior affects trust formation in autonomous vehicles

Innovation

• Real-world testing with Level-4 Robotaxi operations
• Repeated pedestrian interactions at uncontrolled intersections
• Multiple questionnaire scales measuring trust evolution