Towards enhanced safety and enriched infotainment for connected vehicles: modeling, design and implementation
Notice bibliographique
Résumé
Automobile manufacturers are actively delivering a new generation of connected vehicles. These vehicles are driving a far-reaching revolution in the modern society. They will not only save a huge amount of lives and property from traffic accidents, but also fundamentally change the way we travel. As an essential building block of connected vehicles, Vehicle-to-Vehicle (V2V) communication technologies have become a major research and development priority of both governments and car manufacturers. Driving safety and In-Vehicle Infotainment (IVI) services are two primary categories of services enabled by V2V communications. They are not only complementary to, but also mutually beneficial to, each other. On one hand, enhancing driving safety is the most critical issue in current traffic systems. A study led by the U.S. Department of Transportation (U.S. DOT) estimated that V2V technologies can avoid 74 percent of car accidents, potentially saving thousands of lives and billions of dollars each year. Infotainment, on the other hand, not only provides extra encouragement to consumers in purchasing V2V devices, but also brings large economic incentives to manufactures in increasing the market penetration of V2V devices. As a result of this increased penetration, each vehicle can gather more information from other surrounding vehicles, leading to a large improvement in the safety of the whole traffic system. In return, enhanced safety allows everyone to better enjoy infotainment services during reassuring journeys. In this sense, safety and infotainment services are mutualistic in the vehicular ecosystem. In this thesis, we focus on two promising V2V technologies, i.e., the Dedicated Short-Range Communication (DSRC) technology for driving safety and the in-cabin Wi-Fi technology for vehicular infotainment. While DSRC has been recognized by U.S. DOT as the enabling technology of the Intelligent Transportation System (ITS), the in-cabin Wi-Fi technology is recently deployed by many car manufacturers, such as General Motors, Ford, BMW, and Mercedes, to enhance travelling experience for both drivers and passengers. We first characterize these new technologies and their unique features with analytical models, and validate these models with extensive simulations. We then manage to improve the performance of these technologies with several novel solutions. In this way, we not only enhance the driving safety, but also provide better Quality of Service (QoS) for IVI. We implement these technologies in evaluation platforms, and conduct both analytical and simulation analyses to evaluate their communication reliability, efficiency and fairness. We further implement and test them on real test-beds to demonstrate their large improvements over the state of the art.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».