Effects of In-Vehicle Navigation on Perceptual Responses and Driving Behaviours of Drivers at Tunnel Entrances: A Naturalistic Driving Study
Bibliographic record
Abstract
The perceptual responses and driving behaviours of drivers at tunnel entrances vary, which could cause interference and accidents. This study investigated the effects of in-vehicle navigation on the perceptual responses and driving behaviours and whether these effects are actually valid for safety improvement. For this purpose, a series of naturalistic driving experiments was conducted and a comparative analysis was performed considering two different experiment conditions, control condition and in-vehicle navigation condition. Under each condition, the performances of twenty drivers at seven tunnels were evaluated. The area from 200 m outside the tunnel portal to 200 m inside the tunnel portal was averagely divided into four zones. In each zone, two types of perceptual responses (visual responses and psychological responses) and driving behaviours were analysed using six indicators: number of fixations, average duration of fixations, time interval between continuous R-waves, skin conductance response, speed difference in zones, and maximum deceleration. The results showed that in-vehicle navigation significantly affects the perceptual responses and driving behaviours of drivers, and these effects varied in different zones of the tunnel entrance. Furthermore, in-vehicle navigation was found to be valid for safety improvement because beneficial changes in four of the six indicators proved to be effective at appropriate zones. The remaining two indicators, average duration of fixations and maximum deceleration, were not valid, implying that the difficulty of driving information cognition and driving comfort could not be improved by in-vehicle navigation. Moreover, a negative correlation was discovered between the number of fixations and speed difference in zones. This study provides engineers a new knowledge by extending the quantifiable approaches to the analyses of the effectiveness of the effects of in-vehicle navigation.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".