Evidence map›Paper›PMID 35898039›Full record

ReviewSensors (Basel, Switzerland)2022

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review.

Guangzhen Cui, Weili Zhang, Yanqiu Xiao, Lei Yao, Zhanpeng Fang

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Guangzhen CuiMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Weili ZhangMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Yanqiu XiaoMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
Lei YaoMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.ORCID 0000-0002-3507-8209
Zhanpeng FangMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou 450002, China.ORCID 0000-0003-1254-0891

Funding

2019 Youth Top Talent Project ZYQR201912162National Natural Science Foundation of China 51805490scientific and technological research 222102220013Zhengzhou major project 2020CXZX0046
6 · The paper itself

Abstract

Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image-point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

Indexed as

Automobile DrivingWireless TechnologyInternetPerceptionTechnologyautonomous drivingcongestion controlcooperative perceptionC-V2XDSRCIoVmulti-sensor information fusion

Identifiers

PMID35898039
PMCPMC9332497

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.