Assessment of Driving With the Global Positioning System and Video Technology in Young, Middle-Aged, and Older Drivers
Bibliographic record
Abstract
BACKGROUND: Driving is a complex task that is difficult to fully characterize objectively or in a blinded fashion. The main objective of this study was to determine the usefulness of the global positioning system (GPS) and video technology for examining age-related differences in driving. In this study, GPS was used to determine the position, velocity, and acceleration of a vehicle, driven by subjects of different ages, while video footage was used to provide a detailed context of the drive. METHODS: Twenty-four subjects who were young (20 to 29; n = 6), middle-aged (30 to 64; n = 8), and older (65 years of age and older; n = 10) drove their own vehicles on a 30-km route of various types of roads, with a GPS receiver and video camera recording. RESULTS: The combination of GPS and video data allowed for the determination of many age-related driving differences. The young subjects drove faster, had a shorter deceleration distance and time, as well as a shorter acceleration time. Young subjects also had a substantially higher number of infraction demerit points primarily due to speeding, not stopping fully at stop signs, and following too closely. Although the older subjects had a smaller number of demerit points assessed, they tended to make different types of errors than the young subjects, including not stopping at all at a stop sign and turning errors. CONCLUSIONS: GPS and video technology offer new opportunities for the assessment of age-related driving performance.
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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 | 0.001 | 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".