Pedestrian Behavior and Interaction With Autonomous Vehicles
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Summary
This study investigates pedestrian behavior and interaction with autonomous vehicles (AVs), addressing the challenge that pedestrians traditionally rely on implicit cues like eye contact and gestures to negotiate crossings with human drivers. The absence of a human driver in AVs eliminates these communication channels, potentially leading to unpredictable pedestrian behavior and safety risks. The research aims to determine the impact of AVs on pedestrian metrics such as gap acceptance, waiting time, and acceleration, while also examining psychophysiological responses and public perception. The methodology comprised three phases. First, a questionnaire survey was administered to 85 participants from the University of Connecticut community to assess public awareness, trust, and expectations regarding AV technology. Second, pedestrian behavior was analyzed using a Virtual Reality (VR) simulation environment built with RFPro, allowing participants to interact with simulated AVs and human-driven vehicles. Third, psychophysiological data, specifically Electrodermal Activity (EDA), was collected using wearable sensors to measure stress and emotional responses during VR interactions. The study also developed a modified Social Force Model (SFM) to quantify pedestrian dynamics, incorporating repulsive forces from AVs and attractive forces from crosswalk boundaries. The survey results indicated that approximately 95% of participants were aware of AVs, with most possessing some knowledge of the technology. However, significant concerns regarding safety and trust persisted, consistent with broader literature indicating that perceived safety is a primary barrier to AV acceptance. The VR simulation and SFM analysis focused on comparing pedestrian behaviors across different automation levels. The researchers formulated hypotheses predicting that pedestrians would exhibit higher walking speeds, reduced waiting times, and lower gap acceptance when interacting with AVs compared to human-driven vehicles, alongside specific psychophysiological changes such as increased dermal response during initial crossing phases. The SFM calibration allowed for the simulation of pedestrian dynamics under varying repulsive forces, providing a quantitative framework for understanding how pedestrians navigate around AVs. The significance of this research lies in its contribution to transportation planning and intersection design. By understanding how pedestrians perceive and react to AVs, the study provides insights necessary for designing effective external communication interfaces for autonomous vehicles. The findings help identify potential safety issues associated with the lack of implicit communication, offering guidance for mitigating risks through improved traffic control technology and infrastructure. Ultimately, the study supports the development of AV technologies that account for human behavioral patterns, ensuring safer integration of autonomous vehicles into shared urban spaces.
Key finding
The study outlines a comprehensive framework for analyzing pedestrian-AV interaction, revealing high public awareness but significant safety concerns and mistrust in autonomous vehicle technology among surveyed participants.
Methodology
mixed_methods
Sample size: 85
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 (7 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| 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 | 4 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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