209 The Mental Workload of the Driver by Vehicle Velocity and Acceleration
DOI: 10.1299/jsmetokai.2006.55.71
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Summary
This study investigates the relationship between vehicle dynamics and the mental workload of drivers, aiming to quantify cognitive load through physiological responses. The research is motivated by the need to understand how changing driving environments and vehicle motions induce mental stress, which can lead to judgment errors or delayed operations. While subjective evaluations and biological reactions are common methods for assessing mental workload, this paper specifically focuses on heart rate changes as a physiological indicator correlated with vehicle velocity and acceleration. The experimental design involved five subjects driving a 1500cc automatic transmission vehicle on a 1km general road course featuring five distinct sections, including curves with varying radii and a decline. Each subject completed two runs. Data collected included instantaneous heart rate and three-axis vehicle acceleration (longitudinal, lateral, and vertical) along with velocity. To assess mental workload, the authors calculated the "heart rate acceleration response" ($V_{tr}$), defined as the slope of the regression line fitted to seven consecutive heartbeats starting from the beginning of each section. A positive slope indicated an increase in heart rate, suggesting increased mental load. To account for individual differences and variability across runs, this value was standardized ($V_{trst}$) using the mean and standard deviation of the heart rate slope. The results demonstrated that standardized heart rate acceleration effectively identified sections where drivers experienced mental load. For instance, in Section 2 (a decline), most subjects showed positive $V_{trst}$ values, indicating stress, whereas Section 5 showed negative values, likely due to relief upon completing the course. Multiple regression analysis was performed to correlate heart rate changes with vehicle motion indicators (velocity and three-axis acceleration). The analysis revealed that specific vehicle motions significantly influenced heart rate depending on the road section. In Section 1, lateral acceleration had a significant positive effect on heart rate. In Section 3, longitudinal acceleration was the primary factor. In Section 2, vertical acceleration was the dominant influence, attributed to the downhill gradient. The regression models achieved high determination coefficients (e.g., 0.965 for one subject), confirming that vehicle motion metrics can reliably predict physiological stress responses. The study concludes that standardized heart rate acceleration is a valid metric for estimating driver mental workload and identifying specific road sections that induce stress. Furthermore, multiple regression analysis allows for the identification of which specific vehicle motion parameters (velocity or specific axis accelerations) contribute to this workload in different driving scenarios. The authors suggest that future research should expand the sample size and consider driver experience and style to further refine the relationship between vehicle dynamics and mental load.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 2 | 2026-08-23 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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Information type
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- Empirical Findings: physiological data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: theory or model