Method for simulator and scenario design assessing cognitive aspects of fitness to drive
Bibliographic record
Abstract
An increasing part of the global population holds a driver’s license. Thus, a greater variety of prerequisites regarding the fitness to drive will occur, increasing the demand for assessing fitness to drive. However, today, there is a lack of internationally agreed upon methods for assessing the fitness to drive. Specifically, there is a need to develop methods to assess cognitive abilities required for driving safely (Hird, Vetivelu, Saposnik, & Schweizer, 2014; Vrkljan, Myers, Crizzle, Blanchard, & Marshall, 2013). The aim of the present project was to develop an objective and scientifically valid method for assessing cognitive aspects of the fitness to drive in a few targeted groups. The aim was to design and implement a mini-simulator for assessing fitness to drive. The target groups included stroke, mild cognitive impairment, ageing and ADHD. A mini-simulator as well as test scenarios for the assessment of cognitive aspects of fitness to drive was designed (see figure 1). A literature review was undertaken regarding previous research on assessing fitness to drive in the targeted groups. The features of the focused diagnosis were studied regarding underlying cognitive impairment with bearing on driving ability. Each scenario of the simulator drive was designed to enable assessment of these cognitive abilities. Examples of diagnose features that were included were risk taking, distraction, impulsivity, inattention, cognitive flexibility, overconfidence, reaction time, responsiveness, neglect, divided attention and memory. A fixed based mini-simulator was built (Figure 1). To assess the cognitive features mentioned, a road stretch was designed. The road included rural road, highway and urban road. The speed limits varied as well as the landscape surrounding the road. Along the road, different, more or less critical situations, were staged enabling assessment of the targeted cognitive abilities. The mini-simulator met the expectations regarding a good implementation of the simulated scenarios. Future research include validation of the mini-simulator and the scenarios. References Hird, M. A., Vetivelu, A., Saposnik, G., Schweizer, T. A. (2014). Cognitive, On-road, and simulator-based Driving Assessment after Stroke. Journal of Stroke and Cerebrovascular Diseases, 23(10), 2654-2670. Doi.org/10.1016/j.jstrokecerebrovasdis.2014.06.010 Vrkljan, B. H., Myers, A. M., Crizzle, A. M., Blanchard, R. A., & Marshall, S. C. (2013). Evaluating medically at-risk drivers: A survey of assessment practices in Canada. Canadian Journal of Occupational Therapy, 80(5), 295-303. Doi: 10.1177/0008417413511788
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".