Use of video technology and GPS as a tool for driver education – a preliminary investigation with older drivers
Bibliographic record
Abstract
Driver education programs have traditionally taken two general forms: in-classroom or in-vehicle. This study explores a variation on traditional in-vehicle driver education programs by using video technology instead of a driver educator in the passenger seat. A program of this type would be appropriate for currently licensed drivers. Advantages of using video technology include: the possibility of driver behaviour more like their everyday driving, increased safety for the driver educator, and more effective instruction. In this study, 8 subjects aged 70 and older drove a 26 km road course in Winnipeg, Manitoba, Canada while their driving was recorded. The course included all road types including residential, collector, arterial and expressway. Subjects were shown the video and global positioning system (GPS) speed data some time after performing the drive. Subjects watched the video first without feedback, and then with feedback and instruction from a driver educator. Common feedback from the driver educator included instruction on changing lanes, signaling, and stopping at stop signs. Subjects were given three questionnaires at various stages to evaluate the perceived effectiveness of the program. Subjects all agreed that the program was useful to them and all but one self reported using the lessons from the driver educator in their everyday driving 2 to 4 weeks after the video session. The subjects found watching the video with the driver educator feedback more useful than watching the video without feedback. Using in-vehicle video technology is a new opportunity for driver education programs and is an alternative to in-classroom programs for those looking to update their driving skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".