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    Deep understanding of big geospatial data for self-driving cars

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    Name:
    Deep understanding of big geospatial data for self-driving cars.pdf
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    67.81Kb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Shang, Shuo
    Shen, Jianbing
    Wen, Ji Rong
    Kalnis, Panos cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    InfoCloud Research Group
    Date
    2020-07-28
    Online Publication Date
    2020-07-28
    Print Publication Date
    2020-07
    Submitted Date
    2020-06-07
    Permanent link to this record
    http://hdl.handle.net/10754/664604
    
    Metadata
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    Abstract
    Self-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.
    Citation
    Shang, 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
    Publisher
    Elsevier BV
    Journal
    Neurocomputing
    DOI
    10.1016/j.neucom.2020.06.119
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0925231220311929
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.neucom.2020.06.119
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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