Simulated Lane Departure Warning System Reduces the Width of Lane that Drivers Use
Bibliographic record
Abstract
This paper, from a special issue on driving simulator applications in research and clinical practice, reports on a study of a simulated lane departure warning system. These warning systems are designed to decrease the number of vehicle crashes that result from drivers unintentionally leaving the boundary of their lane (e.g., due to fatigue or distraction). The authors conducted a pilot validation study to examine whether drivers (n = 20) would respond to a simulated in-vehicle lane departure warning device in the simulated environment in a similar fashion to how drivers would respond in the real world. The drivers in the study (aged 18-28 years) completed a 20-minute rural drive in a STISIM Drive® simulator. The lane departure device provided auditory feedback (rumble strip sound) when the vehicle’s front left tire approached or crossed the center line, and when the front right tire approached or crossed the edge line at the side of the road. Results showed that the lane departure warning device decreased the number of edge line crossings during the simulated drive and the number of drivers who crossed the edge line. The use of the simulator also decreased the width of lane that drivers used. However, the device did not decrease center line crossings. Survey results also showed that experimental participants considered this type of device necessary in the real world, appropriate, and effective, and supported the idea of greater implementation. With this validation, the device can be used in additional studies in the areas of driver fatigue, impairment, and distraction.
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 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 | 0.000 | 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".