{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T14:57:29Z","timestamp":1721487449295},"reference-count":82,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Romanian Ministry of Research and Innovation","award":["PN-III-P1-1.2-PCCDI-2017-0917"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,12]]},"abstract":"Abstract<\/jats:title>\n Software architecture and applications for the connected car can process and share a variety of digital content, among which high-definition video and augmented reality (AR) content, toward enhanced driving assistance, navigation and infotainment systems and services. However, several technical challenges need to be overcome to make such systems and services viable and efficient, including dealing effectively with a variety of types of systems, devices and platforms, either installed inside the vehicle or represented by the personal mobile and wearable devices of the drivers and passengers. In this paper, we outline these technical challenges and propose a software solution in the form of an event-based middleware layer by modeling the smart, connected car as a specific type of a smart environment. We employ an adapted version of Euphoria, a recently introduced software architecture for general-purpose smart environments, to implement asynchronous communications among heterogeneous input\/output devices inside the vehicle. We also adapt Euphoria to fit into the four-layer infrastructure model of the connected car. We conduct a technical evaluation of the request-response time performance achieved with the Euphoria middleware for streaming digital content from 1 Mbps (360p@30fps) to 32 Mbps (4K@30fps) on various devices, either integrated in the vehicle, not integrated but used inside the vehicle and devices outside the vehicle (the control condition). Our results show effective live streaming achieved for 2K content at 30fps with the 600 Mbps network (i.e., the connected car) and for 4K content at 30fps with the 1.7 Gbps network envisioned for hyper-connected vehicles. These results open up opportunities for high-definition video and AR applications in the automotive industry.<\/jats:p>","DOI":"10.1093\/iwcomp\/iwaa025","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T19:27:06Z","timestamp":1610047626000},"page":"3-16","source":"Crossref","is-referenced-by-count":10,"title":["Empirical Results for High-definition Video and Augmented Reality Content Delivery in Hyper-connected Cars"],"prefix":"10.1093","volume":"33","author":[{"given":"Ovidiu-Andrei","family":"Schipor","sequence":"first","affiliation":[{"name":"MintViz Lab \u2014 MANSiD Research Center, University \u015etefan cel Mare of Suceava, Suceava 720229, Romania"}]},{"given":"Radu-Daniel","family":"Vatavu","sequence":"additional","affiliation":[{"name":"MintViz Lab \u2014 MANSiD Research Center, University \u015etefan cel Mare of Suceava, Suceava 720229, Romania"}]}],"member":"286","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"2021060510275051800_ref1","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11263-018-1070-x","article-title":"Augmented reality meets computer vision: Efficient data generation for urban driving scenes","volume":"126","author":"Alhaija","year":"2018","journal-title":"Int. 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