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Deep understanding of big geospatial data for self-driving cars

dc.contributor.authorShang, Shuo
dc.contributor.authorShen, Jianbing
dc.contributor.authorWen, Ji Rong
dc.contributor.authorKalnis, Panos
dc.date.accessioned2020-08-16T09:42:00Z
dc.date.available2020-08-16T09:42:00Z
dc.date.issued2020-07-28
dc.date.submitted2020-06-07
dc.identifier.citationShang, S., Shen, J., Wen, J.-R., & Kalnis, P. (2020). Deep understanding of big geospatial data for self-driving cars. Neurocomputing. doi:10.1016/j.neucom.2020.06.119
dc.identifier.issn1872-8286
dc.identifier.issn0925-2312
dc.identifier.doi10.1016/j.neucom.2020.06.119
dc.identifier.urihttp://hdl.handle.net/10754/664604
dc.description.abstractSelf-driving cars are capable of sensing environment and moving with little or no human input. Effective control of self-driving cars based on big geospatial data is one of the promising future directions of intelligent transportation. Specifically, big geospatial data understanding is helpful in acquiring travel behavior, vehicle mobility, traffic flow, nearby environment, and traffic-aware navigation. This special issue contains 10 research articles that present solid and novel research studies in the area of geospatial data analytics for self-driving applications, and 1survey article that investigates existing studies related to self-driving cars. All of the 11 papers went through at least two rounds of rigorous reviews by the guest editors and invited reviewers.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0925231220311929
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, [, , (2020-07-28)] DOI: 10.1016/j.neucom.2020.06.119 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDeep understanding of big geospatial data for self-driving cars
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentInfoCloud Research Group
dc.identifier.journalNeurocomputing
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Electronics Science and Technology of China, China
dc.contributor.institutionInception Institute of Artificial Intelligence, United Arab Emirates
dc.contributor.institutionRenmin University of China, China
kaust.personKalnis, Panos
dc.date.accepted2020-06-29
dc.identifier.eid2-s2.0-85089152804
refterms.dateFOA2020-12-29T10:27:48Z
dc.date.published-online2020-07-28
dc.date.published-print2020-07


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