UTILIZING ADVANCED DRIVER ASSISTANCE SYSTEMS IN LATER LIFE: INSIGHTS FROM A LONGITUDINAL STUDY ON AGING DRIVERS
Bibliographic record
Abstract
Abstract Level 1 and 2 Advanced Driver Assistance System (ADAS) technologies (e.g., adaptive cruise control, blind spot warning, lane keep assist) are predicted to be available in approximately three-fourths of all vehicles worldwide by 2025. To realize the potential of these systems in extending safe mobility for aging adults, it is critical that the driver understands the functionality of the technology and uses the system appropriately. The current study examined demographic and vehicle technology questionnaire data from the Longitudinal Research on Aging Drivers (LongROAD) cohort study which concluded in December 2022. A total of 1,417 participants changed their vehicle throughout the study. There were statistically significant increases in the prevalence of all 15 ADAS technologies examined. Despite increases in prevalence, frequency of using the technologies remained unchanged across 5 years. Frequency of use also varied by functionality of the technology whereby participants reported higher frequency of using technologies that provide alerts, such as blind spot warning, than technologies that take action to assist drivers with vehicle operations, such as adaptive cruise control. Results showed differences in prevalence and use of technologies by income and education, suggesting disparities in access to vehicles with technologies that could help to create a safer driving experience. In consideration of the rapid proliferation of ADAS into the vehicle fleet, increased research into how older drivers learn about and use ADAS technologies will assist in efforts to develop tailored and accessible programs for training older adults to properly utilize ADAS available in their own vehicles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".