Evaluation of the human interaction with automated vehicles on highways

Chand, Cadell; Jashami, Hisham; Wang, Haizhong; Hurwitz, David · 2025 · Crossref

DOI: 10.55329/xwwy8052

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates the interaction dynamics between human-driven vehicles (HVs) and automated vehicles (AVs) on highways, addressing a critical knowledge gap regarding mixed-traffic scenarios during the transition to widespread AV adoption. The research aims to understand how human drivers adjust their behavior, stress levels, and fault attribution when interacting with AVs compared to traditional HVs. The researchers conducted a driving simulator experiment at Oregon State University using a high-fidelity motion-based simulator. Thirty-six participants completed the study, which employed a 2 × 2 factorial design manipulating leading vehicle speed (45 mph vs. 65 mph) and autonomy (AV vs. HV). Participants were also exposed to hard-braking scenarios involving both vehicle types. Physiological stress was measured using galvanic skin response (GSR) sensors, while post-drive surveys assessed comfort levels and fault attribution. Statistical analysis utilized Linear Mixed Effects Models to evaluate headway data and paired t-tests for GSR data. Key findings reveal distinct behavioral and physiological differences based on the leading vehicle type. Drivers maintained shorter headways when following AVs than HVs, indicating higher comfort or trust in AVs. However, this effect was moderated by age: drivers over 34.5 years gave AVs 2% larger headways than HVs, whereas younger drivers gave AVs 18% smaller headways. Physiologically, driver stress, measured by GSR peaks, was 70% higher during hard-braking scenarios involving HVs compared to AVs. Regarding crash attribution, of the 10 crashes observed, participants blamed the leading HV in half of the cases but blamed themselves in all crashes involving AVs. Additionally, no participants over 34.5 reported being "unconcerned" when following an AV, compared to 38% of younger participants. The study concludes that human drivers interact differently with AVs than HVs, exhibiting lower stress and closer following distances, particularly among younger drivers. These findings highlight the necessity of calibrating human driver models to account for these behavioral shifts as AV market penetration increases. The results suggest that age is a significant predictor of interaction behavior, and the tendency for drivers to self-blame in AV-related crashes may have implications for liability and trust frameworks in future mixed-traffic environments.

Key finding

Drivers exhibited significantly higher physiological stress during hard-braking scenarios involving human-driven vehicles compared to automated vehicles, and age significantly influenced the headways maintained behind automated vehicles.

Methodology

simulator

Sample size: 36

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.

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

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• acceptance adoption
• situational awareness
• automation
• automation surprise
• ehmi external hmi
• traffic density

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Methodological Resource: tool software, validation psychometrics