Effect of Environmental Factors and Individual Differences on Subjective Evaluation of Human-like and Conventional Automated Vehicle Controllers
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Summary
This study investigates how environmental factors and individual personality traits influence the subjective evaluation of different automated vehicle (AV) driving styles. While technical safety is a primary focus in AV development, user acceptance and comfort are critical for successful adoption. The research addresses a gap in understanding how users perceive variations in AV control behaviors—specifically comparing human-like controllers to conventional, robotic controllers—and how these perceptions shift based on road geometry, roadside furniture, and driver personality. The experiment utilized a motion-based driving simulator with 24 participants who experienced four distinct driving conditions: a conventional controller that rigidly tracked the lane center; two human-like controllers modeled on human driving data (a slower "Slow" controller and a faster "Fast" controller); and a replay of each participant’s own manual drive. The study employed a within-subjects design across 37 scenarios varying in road type (urban vs. rural), curvature, width, and roadside features (e.g., parked cars, hedges). Participants provided real-time subjective feedback using a binary button-press device to indicate whether they found the controller’s behavior safe, natural, and comfortable. Personality traits, specifically Sensation Seeking scores, were also measured to assess individual differences in preference. Results indicated that participants gave significantly higher positive feedback to the replay of their own manual drive compared to the other controllers, particularly when contrasted with the Fast human-like controller during sharp curves. Environmental context significantly influenced perception; participants reported more negative feedback in urban environments compared to rural settings when experiencing their own replay. Furthermore, the presence of roadside furniture affected user feedback, with this effect becoming more pronounced when the vehicle drove closer to the road edge. Regarding individual differences, participants with low Sensation Seeking scores preferred the slower, human-like controller over the faster variant. The conventional controller, which could not navigate around roadside obstacles, was excluded from certain blocked-road scenarios, highlighting its limitations in complex environments. The findings suggest that human preference for AV controllers is not static but adapts to environmental conditions and individual psychological profiles. Users favor driving styles that are human-like, slower, and adaptive to roadside objects and geometry. The study concludes that future AV controller design should incorporate human-centered feedback, adjusting speed and lateral offset based on environmental complexity and user personality traits. This approach aims to enhance ride comfort, increase trust, and improve the overall acceptance and use of automated vehicles.
Key finding
Participants preferred the replay of their own manual driving style over automated controllers, with low sensation-seeking individuals specifically favoring slower, human-like driving styles that adapt to roadside furniture and road geometry.
Methodology
simulator
Sample size: 24
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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
- speed choice
- steering pattern
- automation surprise
- automation
- passenger motion sickness comfort
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, self report data
- Theoretical Contribution: computational model