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AuthorBuchholz, Michaeldc.contributor.author
AuthorMüller, Johannesdc.contributor.author
AuthorHerrmann, Martindc.contributor.author
AuthorStrohbeck, Jandc.contributor.author
AuthorVölz, Benjamindc.contributor.author
AuthorMaier, Matthiasdc.contributor.author
AuthorPaczia, Jonasdc.contributor.author
AuthorStein, Oliverdc.contributor.author
AuthorRehborn, Hubertdc.contributor.author
AuthorHenn, Rüdiger-Walterdc.contributor.author
Date of accession2022-07-05T12:07:15Zdc.date.accessioned
Available in OPARU since2022-07-05T12:07:15Zdc.date.available
Date of first publication2021-07-12dc.date.issued
AbstractThe on-board sensors’ view of an automated vehicle (AV) can suffer from occlusions by other traffic participants, buildings, or vegetation, especially in urban areas. However, knowledge of possible other traffic participants in the occluded areas is crucial for an energy and comfort optimizing control of an AV. In such a case, information from infrastructure sensors sent via vehicle to anything (V2X) communication can help the AV. Fur such cases, we have developed and prototypically implemented a concept where an infrastructure environment model is generated from infrastructure sensors on a multi-access edge computing (MEC) server of an LTE/5G mobile network. This information extends the AVs’ field of view and is beneficially integrated into their motion planning schemes. In this article, after a description of the modules of our approach, we present and discuss real-world results from our pilot site on a public junction with prototype AVs.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseLizenz Adc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/licenseA_v1dc.rights.uri
KeywordConnected Automated Drivingdc.subject
KeywordField Testdc.subject
KeywordMerging Scenariosdc.subject
KeywordV2Xdc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHAutomated vehiclesdc.subject.lcsh
LCSHField experimentsdc.subject.lcsh
TitleHandling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensorsdc.title
Resource typeWissenschaftlicher Artikeldc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-43715dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-43791-6dc.identifier.urn
GNDAutonomes Fahrzeugdc.subject.gnd
GNDCar-to-Car-Kommunikationdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Mess-, Regel- und Mikrotechnikuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
DOI of original publication10.1109/MITS.2021.3089743dc.relation1.doi
Source - Title of sourceIEEE Intelligent Transportation Systems Magazinesource.title
Source - Place of publicationInstitute of Electrical and Electronics Engineers (IEEE)source.publisher
Source - Volume14source.volume
Source - Issue3source.issue
Source - Year2022source.year
Source - From page106source.fromPage
Source - To page120source.toPage
Source - ISSN1939-1390source.identifier.issn
Source - eISSN1941-1197source.identifier.eissn
EU project uulmICT4CART / ICT Infrastructure for Connected and Automated Road Transport / EC / H2020 / 768953uulm.projectEU
Bibliographyuulmuulm.bibliographie
Project uulmLUKAS / Verbundprojekt: LUKAS - Lokales Umfeldmodell für das kooperative, automatisierte Fahren in komplexen Verkehrssituationen; Teilvorhaben: Infrastrukturseite Datenverarbeitung und kooperative Handlungsplanung / BMWi / 19A20004Fuulm.projectOther
Project uulmMEC-View / Verbundprojekt: Mobile Edge Computing basierte Objekterkennung für hoch- und vollautomatisiertes Fahren - MEC-View; Teilvorhaben: Infrastrukturseitige Fusion und Prädiktion der erkannten Objekte im MECServer / BMWi / 19A16010Iuulm.projectOther


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