Driving simulator use and calibration for work zone merge sign evaluation
Bibliographic record
Abstract
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Work zones on highways have the potential to increase crash risk; therefore, efficient and noticeable signs play a critical role in enhancing highway safety. Some engineers have suggested that the traditional Manual on Uniform Traffic Control Devices (MUTCD) merge sign cannot be easily recognized by the driving public due to its graphical nature. In addition, the advance warning areas in work zones exhibit a higher crash rate than the transition, activity, and termination areas, therefore it is beneficial to focus on signage that will impact the advanced warning area of the work zone. A driving simulator is a virtual reality tool that can simulate different driving scenarios and can complement traditional field work due to its feasibility, safety, and cost-effectiveness. In this thesis, a driving simulator was applied to model six scenarios, including three different types of merge signs: MUTCD (baseline), MoDOT, and Quebec. Each merge sign was tested twice in both right lane closed and left lane closed work zone situations. The lane closure scenarios were alternated out of concern that participants might become accustomed to staying in one specific lane. Participants were randomly assigned to a different scenario sequence, thus avoiding sequence bias. A post-experiment survey and a motion sickness screening questionnaire were used to assess driver impressions of the signage and comfort level with the simulator. The results of both the experiment and survey showed that for three different merge signs, participants sustained comparable speeds when passing work zones. For the MoDOT sign, participants merged earlier into the open lane than they did with the other two signs. The MoDOT sign also resulted in the smallest standard deviation of merge location among the three signs. This means that participants reacted to the MoDOT sign consistently. The work zone with the MUTCD sign resulted in the highest speed differential between the merge location and the work zone lane drop taper. Even though 29.4% participants incorrectly perceived the meaning of MUTCD signs compared with only 3.7% for the other two signs, the participants who did correctly understand the MUTCD sign rated the sign positively.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".