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Deep understanding of big geospatial data for self-driving cars.pdf
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Accepted manuscript
Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
InfoCloud Research Group
Date
2020-07-28Online Publication Date
2020-07-28Print Publication Date
2020-07Submitted Date
2020-06-07Permanent link to this record
http://hdl.handle.net/10754/664604
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Show full item recordAbstract
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.2020.06.119Publisher
Elsevier BVJournal
NeurocomputingAdditional Links
https://linkinghub.elsevier.com/retrieve/pii/S0925231220311929ae974a485f413a2113503eed53cd6c53
10.1016/j.neucom.2020.06.119