A Driving Simulator Investigation on the Aggressiveness of an Automatic Lane-Change System
DOI: 10.1109/access.2025.3579019
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of personalizing automatic lane-change systems in highly automated driving, where reliance on manual driving data for customization becomes increasingly difficult. The authors propose a bidirectional human-machine interaction approach, investigating how the aggressiveness of an automated lane-change system affects drivers’ subjective perceptions and gaze behavior. The research aims to validate whether driver feedback, specifically eye movements, can serve as a basis for adjusting system aggressiveness to suit individual preferences. The researchers conducted a driving simulator experiment involving 22 participants. The simulator featured a fixed motion platform, three-projector visual display, and a Smart Eye Pro remote eye-tracking system. The automatic lane-change system utilized a previously developed fuzzy inference model that adjusted decision-making aggressiveness based on inter-vehicle distances. Three aggressiveness levels were implemented: Aggressive, Medium, and Conservative, defined by different thresholds for lane-change intention. A sinusoidal kinematic model ensured consistent lane-change execution trajectories across all conditions. Data collected included subjective evaluations via questionnaires and continuous gaze patterns, analyzed using Gaze Ratio and moving Gaze Ratio metrics to quantify visual attention toward surrounding vehicles. The results demonstrated that the aggressiveness of the automatic lane-change system significantly influenced both drivers’ subjective perceptions and their gaze behavior. Specifically, system aggressiveness affected safety evaluations and the degree of agreement with the system’s decisions. Furthermore, the study found a direct correlation between system aggressiveness and the drivers’ gaze patterns toward the rear vehicle in the target lane. This indicates that the relative driving status of the rear vehicle is the primary factor influencing drivers’ subjective perceptions during automated lane changes. The findings confirm that drivers’ eye movement behaviors can effectively reflect their subjective experiences and perceptions of the automated system. The significance of this work lies in its validation of a novel personalization strategy for automated driving systems. By demonstrating that driver gaze and subjective feedback respond predictably to system aggressiveness, the study supports the feasibility of using human-machine interaction to fine-tune automated driving styles without relying on historical manual driving data. This approach offers practical suggestions for designing future intelligent vehicles, enabling seamless personalization through real-time driver monitoring. The methodology provides a foundation for enhancing user acceptance and trust in highly automated driving systems by aligning system behavior with individual driver preferences through bidirectional interaction.
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 | 2026-06-05 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-10 |
| promote | success | — | — | — | 1 | 2026-06-05 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model