Cited by 209Autonomous Vehicle Technology and SafetyAdvanced Neural Network ApplicationsRobotics and Sensor-Based Localization

Pedestrian Recognition and Tracking Using 3D LiDAR for Autonomous Vehicle

Wang, Heng, Wang, Bin, Liu, Bingbing, Meng, Xiaoli, Yang, Guanghong·2017·Robotics and Autonomous Systems

Integrity signals

Registry

Crossref · 10.1016/j.robot.2016.11.014

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Automotive LiDAR sensors are supposed to perform blind-spot monitoring, pedestrian identification, obstacle detection, and landscape mapping to ensure reliability and safety during driving operations.

    “Automotive LiDAR sensors are supposed to perform blind-spot monitoring, pedestrian identification, obstacle detection [35], and landscape mapping [59] to ensure reliability and safety during driving operations.”

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Cited by

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Authors
Wang, H., Wang, B., Liu, B., Meng, X., & Yang, G.
Affiliations
Agency for Science, Technology and Research (SG)
Corresponding author
Wang, Heng
Venue
Robotics and Autonomous Systems
Publisher
Elsevier BV
Published
2017-02
Volume
88
Pages
71-78
ISSN
0921-8890 (ISSN-L)
OpenAlex
W2554777878
Cited by
209
References
31
Citations by year
2026: 2 · 2025: 11 · 2024: 19 · 2023: 22 · 2022: 39 · 2021: 33 · 2020: 34 · 2019: 31 · 2018: 11 · 2017: 7
FWCI
14.23
Type
journal article
Language
en
Open access
closed
Locations
Robotics and Autonomous Systems (publishedVersion)
Funders
  • National Natural Science Foundation of China (61673098, 61273148, 61420106016, 61621004)
  • IAPI Fundamental Research Funds (2013ZCX01-01)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
Keywords
pedestrian detection, pedestrian tracking, 3D LiDAR, autonomous vehicles, support vector machine, curb detection, road safety

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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