Training to Support Appropriate Reliance on Advanced Driver Assistance Systems
Notice bibliographique
Résumé
Advanced driver assistance systems (ADAS) are becoming more widely available to consumers. While these systems have potential safety benefits, overreliance on ADAS has contributed to several fatal collisions. Training has been identified as one way to address overreliance and support safe use of driving automation. Currently, existing training and related research generally focuses on teaching drivers various system limitations, but there are mixed results regarding the relationship between knowledge of limitations and trust in and reliance on ADAS. Further, this limitation-focused training approach may not be practical (e.g., due to the large number of limitations to learn and remember). This dissertation aims to understand the relationship between knowledge of ADAS limitations, trust, and reliance, and investigate an alternative to limitation-focused training.First, a survey study was conducted and the results suggested that, in addition to impracticalities associated with limitation-focused training, it may not be an ideal approach if targeting a wide range of drivers, as knowledge did not significantly impact trust or reliance intention for drivers who had ADAS experience. Subsequently, training videos were developed to compare limitation-focused training to a novel training approach which highlighted the drivers’ responsibility when using ADAS (i.e., responsibility-focused training). An online study revealed limited differences between the limitation-focused and responsibility-focused approach. Where significant differences were found for quantitative measures, they indicated potential drawbacks of the limitation-focused approach. Further, results of semi-structured interviews with participants suggest that limitation-focused training may have the unintended consequence of decreased interest in using ADAS among drivers without ADAS experience. Further investigation with a simulator study showed that even when an attention monitoring system was implemented to remind participants to keep their eyes on the road, there was a benefit of the responsibility-focused training, but not limitation-focused training, on reliance (e.g., lower rate of long glances to a secondary task and taking over control of the vehicle sooner when a potential conflict occurred). Overall, this dissertation adds to the existing literature on the relationships between ADAS knowledge, trust, and reliance. Further, it expands the limited literature on alternatives to limitation-focused training and provides preliminary support to the responsibility-focused training approach.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».