Application of Auditory Signals to the Operation of an Agricultural Vehicle: Results of Pilot Testing
Notice bibliographique
Résumé
The operation of agricultural vehicles is a multitask activity that requires proper distribution of attentional resources. Human factors theories suggest that proper utilization of the operator's sensory capacities under such conditions can improve the operator's performance and reduce the operator's workload. Using a tractor driving simulator, this study investigated whether auditory cues can be used to improve performance of the operator of an agricultural vehicle. Steering of a vehicle was simulated in visual mode (where driving error was shown to the subject using a lightbar) and in auditory mode (where a pair of speakers were used to convey the driving error direction and/or magnitude). A secondary task was also introduced in order to simulate the monitoring of an attached machine. This task included monitoring of two identical displays, which were placed behind the simulator, and responding to them, when needed, using a joystick. This task was also implemented in auditory mode (in which a beep signaled the subject to push the proper button when a response was needed) and in visual mode (in which there was no beep and visual, monitoring of the displays was necessary). Two levels of difficulty of the monitoring task were used. Deviation of the simulated vehicle from a desired straight line was used as the measure of performance in the steering task, and reaction time to the displays was used as the measure of performance in the monitoring task. Results of the experiments showed that steering performance was significantly better when steering was a visual task (driving errors were 40% to 60% of the driving errors in auditory mode), although subjective evaluations showed that auditory steering could be easier, depending on the implementation. Performance in the monitoring task was significantly better for auditory implementation (reaction time was approximately 6 times shorter), and this result was strongly supported by subjective ratings. The majority of the subjects preferred the combination of visual mode for the steering task and auditory mode for the monitoring task.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».