Method for simulator and scenario design assessing cognitive aspects of fitness to drive
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».