Characterization of Stable-Health Older Drivers Using Low-Speed Driving Maneuvers From In-Vehicle Sensor Data
Bibliographic record
Abstract
The Candrive study aims to improve the current practices of screening elderly drivers in Canada by identifying predictors of motor vehicle collisions from monitoring their daily driving behaviours using in-vehicle sensors.The thesis objective was to characterize the baseline behaviour of stable-health older drivers by proposing parameters of interest for detecting changes in behaviour and methods to differentiate drivers using their maneuvers.The in-vehicle sensor data from 12 stable-health drivers were processed, and a turn-identification algorithm with 97.7% accuracy was created for extracting four maneuvers: accelerating from stop, decelerating to stop, right turns, and left turns on 40 to 60 km/h roadways.Most of the drivers exhibited relatively steady month-to-month acceleration behaviours and lower accelerations in adverse driving conditions, which represented their typical driving behaviours.Drivers can be differentiated by the driving patterns from their maneuvers using a multi-expert classifier, which may be applicable for detecting changes in driving behaviour.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".