Analysis of EEG Characteristics of Drivers at the Entrance and Exit of an Undersea Tunnel and Research on Driving Safety
Bibliographic record
Abstract
To study the influence of illumination and longitudinal slope at the entrance and exit of an undersea tunnel on driver EEG characteristics, a real vehicle experiment was performed with the Jiaozhou Bay Undersea Tunnel. The experimental data of a driver’s real vehicle experiment were collected using an illuminance meter, EEG instrument, video recorder and other experimental equipment. The EEG power spectrum was classified according to frequency, the difference between the EEG power spectrum at the entrance and exit sections and other regions was analyzed, and the influence of the illumination and longitudinal slope of the undersea tunnel on the brain activity of drivers was studied. The region near the entrance and exit of the undersea tunnel was divided equidistantly, the changes in the EEG power of the driver during the process of entering and exiting the undersea tunnel were analyzed, and the changes in brain activity and different brain regions during the process were studied. Based on the EEG power, the model of illumination, longitudinal slope and their coupling effect was established. The traffic safety of the entrance and exit of the undersea tunnel was analyzed, and a high-risk driving region was found. The results show that the power spectrum of the entrance and exit sections of the undersea tunnel is obviously different from those of other sections. At 50 m behind the entrance point and 50 m in front of the exit point of the undersea tunnel, the power of the β wave changes rapidly and is at a high level. The consistency between the variation law of the β wave and the variation law of illumination is high. At the entrance and exit of the undersea tunnel, the active regions of the driver’s brain are concentrated in the frontal lobe and occipital lobe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".