1A1-G05 Development of a Driving Simulator to Evaluate User Experience for Advanced Intelligent Vehicles
DOI: 10.1299/jsmermd.2015._1a1-g05_1
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Summary
This paper addresses the need to evaluate user experience (UX) differences between conventional human-driven vehicles and advanced intelligent vehicles capable of autonomous driving. As autonomous vehicles approach commercial availability, understanding how drivers react to these systems compared to manual control is critical for building beneficial human-vehicle relationships. While real-world experiments offer ideal conditions, they suffer from high costs, safety risks, and lack of reproducibility. To overcome these limitations, the authors developed a cost-effective driving simulator using the Unity development engine and accessible hardware to analyze individual driving experiences across various road conditions. The experimental design involved a virtual reality environment constructed with 3D models of roads, vehicles, pedestrians, and buildings. The simulator featured two distinct human-machine interfaces: a conventional setup using a Logitech G27 steering wheel and pedals for manual driving, and an autonomous setup using a Microsoft Surface Pro 3 tablet for destination input via touch. The virtual course spanned approximately 2 km across four regions: highway, urban area, residential area, and parking lot, incorporating specific events such as lane closures, sudden stops, and pedestrian crossings. Twelve university students participated, divided into novice drivers (less than two years of experience) and experienced drivers (two to eight years). Participants completed trials in both manual and autonomous modes, with performance metrics including arrival time and collision counts. Subjective workload was assessed using the NASA-TLX scale, and preferences were analyzed through free-description questionnaires. The results indicated that autonomous driving significantly reduced subjective workload for all participants. Specifically, mental workload decreased by 41.1% for novices and 49% for experienced drivers compared to manual driving. Preferences for driving modes varied distinctly by experience level. Experienced drivers predominantly preferred manual driving, citing the enjoyment of driving, flexibility, and reliability as key factors. In contrast, novice drivers tended to prefer autonomous driving, primarily due to the lack of required effort and the independence from driving skills. On highways, novices showed an 83% preference for autonomous mode, largely because merging maneuvers imposed a significant mental burden during manual driving. The study concludes that driver experience significantly influences the acceptance and preference of autonomous driving technologies. While autonomous systems reduce cognitive load and appeal to novices seeking ease and safety, experienced drivers value the flexibility and engagement of manual control. These findings highlight the importance of tailoring autonomous vehicle interfaces and features to different user groups to enhance overall user experience and facilitate the broader adoption of advanced intelligent vehicles.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| 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 | failed | — | — | — | 3 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 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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- Methodological Resource: tool software, validation psychometrics
- Theoretical Contribution: computational model