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Smart Cars Need Smart Roads

2015· article· en· W2179990383 sur OpenAlexaboutno aff
John Blau

Notice bibliographique

RevueResearch-Technology Management · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueVehicular Ad Hoc Networks (VANETs)
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEuropean unionGross domestic productQuarter (Canadian coin)BusinessGreenhouse gasProduct (mathematics)Transport engineeringEngineeringEconomic growthEconomic policyGeographyEconomics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

As Apple, Google, and other high-tech companies team with automakers to make cars smart enough to drive themselves, do roads need to be as dumb as the concrete and asphalt they're made of? Europe doesn't think so. The European Union has invested more than 200 million [euro], or $227 million, over the past few years in a number of research programs aimed at creating intelligent infrastructure that will communicate with smart cars. The goal is to eliminate congestion in growing urban areas, help the environment, and above all, save lives. The costs of auto travel in Europe are large, multifarious, and growing. European Union drivers currently own one third of the world's one billion cars, and congestion costs the region about 1 percent of gross domestic product (GDP) every year, a number that is rising. Transport is also responsible for about a quarter of EU greenhouse gas emissions, which Brussels aims to reduce by as much as 80 percent of 1990 levels by 2050. And nearly 27,000 people died on European roads last year. On average, up to 50 million people are injured in car accidents in Europe each year, with about 600,000 hospitalized at a cost of about 160 billion [euro], according to figures compiled by the World Health Organization and the European Transport Safety Council. Against this background, the European Union has made intelligent transport a priority in its research and innovation programs. Smart roads, capable of warning drivers of hazardous road conditions and approaching cars well before such hazards enter their field of vision, are a leading component of the effort. Powering this smart infrastructure is the latest advances in sensors, wireless communications, and computers, all tied together by the Internet. Smart roads are one part of a system designed to provide vehicles with 360-degree awareness of their surroundings via a set of in-car sensors, transmitters, and processors that allow cars to communicate with each other and gather real-time data from road infrastructure, including signs and traffic signals as well as the road itself. Within this system, roads would incorporate sensors and other technology to provide vehicles and drivers information about hazardous conditions and other critical events, well before they're within eyeshot. The technology, known as vehicle-to-everything, or V2X, could reduce accidents by as much as 80 percent, European researchers claim. Estimates for developing and implementing this advanced road transport infrastructure across Europe range between 80 billion [euro] and 140 billion [euro]. Hermann Mezer, the chief executive of the European Road Transport Telematics Implementation Coordination (ERTICO), a public-private organization involved in the production of intelligent transport systems, called the systems a game changer. Not surprisingly, a number of technology players want to carve out a piece of that business, among them Germany's Siemens, NXP in the Netherlands, and a number of US companies, including Cisco and IBM. Siemens, a key provider of traffic management systems in Europe, has been quick to expand into intelligent infrastructure, especially environmental detection systems and road works warning systems. The Munich-based engineering giant is working closely with NXP, a specialized chipmaker headquartered in Einhoven that is among the first to develop and deliver V2X chipsets for high-volume manufacturing. The Dutch company is also collaborating with Singapore's Nanyang Technological University to build a smart mobility test bed in the southeast Asian city-state. The project involves 100 vehicles and 50 roadside units that will test V2X technologies over the next four years. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,131
Score d'incertitude au seuil0,439

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,002
Communication savante0,0120,023
Science ouverte0,0020,007
Intégrité de la recherche0,0100,008
Charge utile insuffisante (le modèle a refusé de juger)0,1310,108

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,295
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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