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Deep understanding of big geospatial data for self-driving: Data, technologies, and systems.

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Title: Deep understanding of big geospatial data for self-driving: Data, technologies, and systems.
Authors: Wang, Haiyan1 (AUTHOR) imwhyy@std.uestc.edu.cn, Feng, Jiaming1 (AUTHOR) jiamingvon@std.uestc.edu.cn, Li, Ke1 (AUTHOR) like_like@std.uestc.edu.cn, Chen, Lisi1 (AUTHOR) lchen012@e.ntu.edu.sg
Source: Future Generation Computer Systems. Dec2022, Vol. 137, p146-163. 18p.
Subjects: Object recognition (Computer vision), Geospatial data, Lane changing, Autonomous vehicles, Software engineers, Software engineering
Abstract: With the continued development of Autonomous Vehicle System (AVS), self-driving related technologies have attracted much attention over the past decade. In this light, we survey existing literature regarding self-driving related data, technologies, and systems. We present details of representative studies regarding collision avoidance, automatic lane-changing maneuver, object detection (including pedestrian detection and obstacle detection), and vehicle trajectory prediction, respectively. This survey summarizes the findings of existing self-driving studies, thus uncovering new insights that may guide researchers and software engineers in fields of self-driving data management systems and autonomous vehicle systems. • Survey studies regarding autonomous vehicle systems and discuss emerging methods. • Classify and compare existing methods regarding automatic lane change maneuver. • Classify and compare existing solutions to obstacle detection. • Discuss emerging problems and solutions regarding vehicle trajectory prediction. [ABSTRACT FROM AUTHOR]
Copyright of Future Generation Computer Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Deep understanding of big geospatial data for self-driving: Data, technologies, and systems.
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  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Lane+changing%22">Lane changing</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineers%22">Software engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink>
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  Data: With the continued development of Autonomous Vehicle System (AVS), self-driving related technologies have attracted much attention over the past decade. In this light, we survey existing literature regarding self-driving related data, technologies, and systems. We present details of representative studies regarding collision avoidance, automatic lane-changing maneuver, object detection (including pedestrian detection and obstacle detection), and vehicle trajectory prediction, respectively. This survey summarizes the findings of existing self-driving studies, thus uncovering new insights that may guide researchers and software engineers in fields of self-driving data management systems and autonomous vehicle systems. • Survey studies regarding autonomous vehicle systems and discuss emerging methods. • Classify and compare existing methods regarding automatic lane change maneuver. • Classify and compare existing solutions to obstacle detection. • Discuss emerging problems and solutions regarding vehicle trajectory prediction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Future Generation Computer Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=158957222
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      – Type: doi
        Value: 10.1016/j.future.2022.07.003
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 18
        StartPage: 146
    Subjects:
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Geospatial data
        Type: general
      – SubjectFull: Lane changing
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Software engineers
        Type: general
      – SubjectFull: Software engineering
        Type: general
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      – TitleFull: Deep understanding of big geospatial data for self-driving: Data, technologies, and systems.
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            NameFull: Wang, Haiyan
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            NameFull: Feng, Jiaming
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            NameFull: Li, Ke
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            NameFull: Chen, Lisi
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          Dates:
            – D: 01
              M: 12
              Text: Dec2022
              Type: published
              Y: 2022
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              Value: 137
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