Supporting Safe Driving for Older Adults - at a Crossroads with ADAS [Roadmap for Measurement and Applications]
Bibliographic record
Abstract
The automobile is considered essential for transportation in most western countries. For many older adults, driving is a key enabler for maintaining an active, engaged, and independent lifestyle, allowing the freedom to be able to get out for work, social activities, shopping, physical exercise, and many other activities. Thus, driving leads to physical activity, cognitive stimulation, and social engagement that benefit and support an ongoing active life. As we age, we all experience the natural and illness-related declines associated with aging which can affect our ability to drive safely. The challenge is how do we ensure drivers continue to have the required skills for safe driving across the lifespan. This paper proposes a new role for advanced driver assistance systems (ADAS) technologies in extending driver capabilities into older age. These ADAS technologies can take on two roles: first, acting as assistive devices enhancing safety and convenience for drivers in day-to-day situations, and second, as measurement systems to assess age-related declines in 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".