Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study
DOI: 10.1109/TITS.2025.3534879
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Summary
This study investigates how driving context, specifically weather conditions and traffic situations, influences passenger preferences for lateral driving styles in autonomous vehicles (AVs). The research is motivated by the need to enhance AV acceptance and trust, as mismatches between human expectations and AV behavior can cause insecurity despite technical safety. While prior research has focused on longitudinal driving styles or highway scenarios, this work addresses a gap in understanding lateral preferences on rural roads under varying environmental conditions. It specifically tests the common assumption that passengers prefer AVs to mimic their own driving behavior. The researchers conducted a controlled subject study using a high-fidelity driving simulator equipped with a motion system to ensure realistic proprioceptive feedback. Forty-two German participants experienced four distinct lateral driving styles: passive, "like on rails" (strict lane-centering), sportive, and a style mimicking their own recorded behavior. These styles were tested under two weather conditions (clear and adverse) and various traffic scenarios, including interactions with oncoming traffic. Data collection involved multiple instruments: the Multidimensional Driving Style Inventory (MDSI) for self-assessment, the Automated Ride Comfort Assessment (ARCA) and Trust in Automated Systems (TiA) questionnaires for post-drive evaluation, and continuous on-drive comfort ratings. The study also introduced a novel reactive driving behavior model to emulate human-like curve negotiation while responding to oncoming traffic. The results revealed a notable preference for a more passive driving style among participants, contradicting the hypothesis that subjects prefer to be driven in a manner that mimics their own behavior. Statistical analysis of participant responses indicated that weather conditions and the presence of oncoming traffic substantially influenced perceived comfort and trust. Specifically, adverse weather and oncoming vehicles, particularly trucks, heightened the demand for conservative lateral positioning. The study found that strict lane-centering ("like on rails") was less preferred than the passive style, which allowed for more natural trajectory adjustments. Furthermore, the research validated the use of the MDSI and other questionnaires in assessing driving style preferences in simulated environments. The significance of this work lies in its challenge to the prevailing assumption that personalized AV driving should mirror the user's manual driving habits. Instead, it suggests that passengers prioritize comfort and perceived safety, often favoring a passive style regardless of their own driving aggressiveness. The findings highlight the critical role of contextual factors like weather and traffic in shaping AV acceptance. By providing a publicly accessible dataset and demonstrating the impact of lateral behavior on user experience, the study offers valuable insights for developing more acceptable and comfortable autonomous driving policies, particularly for rural road scenarios.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 13 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | normalization | — | — | 2 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Theoretical Contribution: computational model