Vehicle Height Affects Drivers’ Speed Perception: Implications for Rollover Risk
Bibliographic record
Abstract
The North American vehicle fleet has evolved in recent years to include an increasing percentage of pickups, sport utility vehicles (SUVs), and minivans. In 2002, sales of light trucks and vans accounted for almost half of all Canadian vehicle sales. The increased popularity of SUVs has been particularly striking, with a 143% increase in sales since 1993. Typically larger and heavier than automobiles, SUVs are built on frames that are more rigid and that ride higher, characteristics that not only provide increased physical protection to their occupants but also add to their overall appeal. SUVs, however, are involved in fatal rollover crashes at a much higher rate than cars. While this is undoubtedly due in part to their stiffer frames and higher centers of gravity, the manner in which SUVs are driven may also play a role. SUV drivers are often anecdotally reported to be overconfident, tending to overestimate their vehicle’s capabilities. Evidence suggests that because they sit higher, drivers of SUVs (and vans and pickups) are less able to judge speed accurately. A study was conducted to assess drivers’ chosen speed when they operated a simulated vehicle while viewing the road from a low eye height and a high eye height. Participants were instructed to drive, without reference to a speedometer, at a highway driving speed at which they felt comfortable and safe. As expected, drivers seated at a high eye height drove faster than when they were seated at a low eye height. The influence of driver eye height and lead vehicle size (large versus small) on the following distance from a slower-moving lead vehicle was also investigated. Regardless of eye height, the differences in following distances suggest that the size of the lead vehicle may affect how closely drivers choose to follow it.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".