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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]
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Database: Engineering Source
Description
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]
ISSN:0167739X
DOI:10.1016/j.future.2022.07.003