Towards enhanced safety and enriched infotainment for connected vehicles: modeling, design and implementation
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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 [1] 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 ii further implement and test them on real test-beds to demonstrate their large improvements over the state of the art.This thesis represents not only my work in front of the screen, it is also a milestone in almost five years of my study, research and life at McGill University and especially the Cyber-Physical System Laboratory.The path to my doctoral degree was rugged, along with many intellectual challenges and psychological frustrations.I could never overcome them without the valuable help, support, advice and suggestions from many good and honest, brilliant and professional people.Their love and kindness have changed my toughest years into an enjoyable and rewarding experience.First and foremost, I would like to thank my advisor, Professor Xue Liu, for being supportive since day one.This thesis could never be accomplished without his valuable guidance and continuous encouragement.He has been always patient with me, and thoroughly taught me how to become a good researcher and scientist.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| 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 découle