Notice bibliographique
Résumé
With vehicular ad hoc networks gaining an ever-increasing interest to serve a diverse variety of applications in today’s intelligent transportation systems, it was not at all surprising for the guest editorial team to receive a handful of submissions for this special issue addressing different aspects and test-beds of vehicular networks. In sum, 8 papers were accepted to be published in the special issue. An interesting note to make is that 5 of the accepted papers had an actual experimental implementation carried out in the road and under real-world conditions. This certainly helps to justify their application and usefulness for future deployment by the industry and authorities.While all papers address enhancing the safety and efficiency of driving, each of them addresses a certain aspect of this issue. The paper by M. J. Flores et al., “Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions,” seeks to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index under varying light conditions (diurnal and nocturnal). In their paper “Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network,” D. Houli et al. propose a new multiobjective control algorithm based on reinforcement learning for urban traffic signal control, named, multi-RL. M. Tsukada et al. in “Design and Experimental Evaluation of a Vehicular Network Based on NEMO and MANET,” present a policy-based solution to distribute traffic among multiple paths to improve the overall performance of a vehicular network. The paper “Traffic Data Collection for Floating Car Data Enhancement in V2I Networks” by D. F. Llorca et al. presents a complete vision-based vehicle detection system for floating car data (FCD) enhancement in the context of vehicular ad hoc networks. S. Miyata et al. in “Improvement of Adaptive Cruise Control Performance” propose a more accurate method for detecting the preceding vehicle by radar while cornering. The paper “Reducing Congestion in Obstructed Highways with Traffic Data Dissemination Using Ad hoc Vehicular Networks” by T. D. Hewer et al. presents a message-dissemination procedure that uses vehicular wireless protocols to influence vehicular flow, reducing congestion in road networks. M. Koubek et al., in “Reliable Delay ConstrainedMultihop Broadcasting in VANETs,” focus onmechanisms that improve the reliability of broadcasting protocols, where the emphasis is on satisfying the delay requirements for safety applications. Finally, M. G. Cinsdikici and K. Memis in “Traffic Flow Condition Classification for Short Sections Using Single Microwave Sensor” seek to identify the current traffic condition by examining the traffic measurement parameters and taking into account occupancy as another important parameter of classification. We hope this special issue can help the research community further its understanding of this emerging field.
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 | 0,001 | 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 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| 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 ».