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PublisherUniversität Ulmdc.publisher
Dewey Decimal GroupDDC 600 / Technology (Applied sciences)dc.subject.ddc
TitleDynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labelingdc.title
Resource typeBeitrag zu einer Konferenzdc.type
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Mess-, Regel- und Mikrotechnikuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
Source - Title of source2018 IEEE International Conference on Robotics and Automation (ICRA)source.title
Quellenangabe - HerausgeberLynch, Kevinsource.contributor.editor1
Source - PublisherNew York, NYsource.publisherPlace
Source - Place of publicationInstitute of Electrical and Electronics Engineerssource.publisher
Source - Year2018source.year
Source - From page2056source.fromPage
Source - To page2063source.toPage
Source - SerialIEEE International Conference on Robotics and Automation ICRAsource.series
Source - ISSN1050-4729source.identifier.issn
Source - ISBN978-1-5386-3081-5source.identifier.isbn
EU projectRobustSENSE / Robust and Reliable Environment Sensing and Situation Prediction for Advanced Driver Assistance Systems and Automated Driving / EC / H2020 / 661933uulm.projectEU
Conference nameIEEE International Conference on Robotics and Automation (ICRA)uulm.conferenceName
Conference placeBrisbaneuulm.conferencePlace
Conference start date2018-05-21uulm.conferenceStartDate
Conference end date2018-05-25uulm.conferenceEndDate
CommunityFakultät für Ingenieurwissenschaften, Informatik und

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