Evaluation of Mental Load of Drivers in Long Highway Tunnel Based on Electroencephalograph
Bibliographic record
Abstract
In recent years, the mileage of the tunnels has substantially increased with the rapid highway construction that led to increasing highway tunnels. Most studies on tunnel accidents have mainly focused on the external environments, such as tunnel structure, traffic volume, and lighting. In addition, although many studies on mental load of drivers have been conducted for public roads, such studies for highway tunnels have been limited. In this study, three scenarios with different front vehicle speeds (60, 45, and 30 km/h) in a two-lane long tunnel (one lane in each travel direction) were evaluated using a driving simulator. The experiment involved 24 participants (14 men and 10 women) with an average age of 25.8 years and an average experience of 3.2 years. The electroencephalogram (EEG) technology was used to collect the leading EEG indicators during the driving simulation of the scenarios: α, β, and θ waves and the wave ratio, (α + θ)/β. According to the β-wave energy measurements, the alertness of drivers was the lowest at 45 km/h after adapting to the tunnel environment, indicating that the drivers were more comfortable at this speed. This preliminary finding should help in determining the speed limit in this type of tunnel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".