Bridging the gap: understanding the factors affecting pedestrian safety perceptions in the age of driverless vehicles

Rezwana, Saki; Shaon, Mohammad Razaur Rahman; Lownes, Nicholas; Jackson, Eric · 2025 · DOAJ

DOI: 10.55329/pjax7195

archive: archived pipeline: cataloged verified

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Summary

This study investigates how pedestrians perceive safety and react to driverless vehicles (DVs) compared to human-driven vehicles (HDVs), addressing a critical gap in understanding pedestrian behavior in mixed-traffic environments. The research is motivated by the disruption of traditional non-verbal communication cues, such as eye contact and gestures, which pedestrians rely on to gauge yielding intentions. The absence of these cues in DV interactions may increase crash risks and alter crossing behaviors, necessitating empirical data to inform safer infrastructure and communication strategies. The researchers employed a three-phase methodology involving 41 participants from the University of Connecticut and surrounding communities. The study began with a preliminary questionnaire to assess public perception, education levels, and familiarity with automation. The core experimental phase utilized immersive virtual reality (VR) simulations to mimic real-world traffic scenarios at a four-way signalized intersection. Participants navigated seven distinct scenarios involving no vehicles, HDVs, DVs, and mixed traffic (50% DV/50% HDV) under both day and night conditions. The VR environment was integrated with Simulation of Urban Mobility (SUMO) to manage traffic logic. During the simulations, the study measured two key metrics: Gap Acceptance (GA) time, defined as the interval a pedestrian accepts before crossing, and psychophysiological stress responses using Electro-dermal Activity (EDA) sensors, specifically Galvanic Skin Response (GSR). Data was synchronized and filtered to remove motion artifacts, resulting in 1,066 analyzed trials. The results indicated significant differences in both physiological and behavioral responses across scenarios. EDA analysis revealed that interactions with DVs elicited the highest mean GSR values (4.72), indicating elevated stress levels compared to HDVs (2.58), mixed traffic (3.49), and no-vehicle scenarios (1.45). This heightened stress response is attributed to the novelty and unpredictability of DVs. Behaviorally, pedestrians exhibited significantly longer GA times when interacting with DVs than with HDVs, suggesting a more cautious crossing strategy driven by uncertainty or mistrust of autonomous technology. Questionnaire data further showed that while awareness of autonomous technology was high (97.62%), only 5% of participants considered themselves highly knowledgeable, and perceptions varied by age and education level. The findings underscore that the transition to driverless vehicles significantly impacts pedestrian dynamics, increasing both physiological stress and hesitation in crossing decisions. These results imply that current pedestrian models and traffic signal timings may require adjustment to accommodate longer wait times and prevent congestion. The study highlights the need for standardized, intuitive communication methods from DVs to restore pedestrian trust and safety. Ultimately, the research provides empirical evidence to support the development of safer autonomous systems and infrastructure designs that account for the complex human factors involved in pedestrian-DV interactions.

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Topics

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• pedestrian behavior perception
• acceptance adoption