ReviewSensors (Basel, Switzerland)2022
Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review.
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.
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.
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.
Who cites it
12 citing papers in PubMed.
- Vehicle Autonomy to Ecosystem Intelligence: A Systematic Review of Dynamic Vision Architectures in Surface Mining Operations.Sensors (Basel, Switzerland) · 2026Review
- A Hybrid Deep Learning Architecture for Content Request Prediction in the Internet of Vehicles.Sensors (Basel, Switzerland) · 2026Article
- Review of Uneven Road Surface Information Perception Methods for Suspension Preview Control.Sensors (Basel, Switzerland) · 2025Review
- Security for the Internet of Vehicles with Integration of Sensing, Communication, Computing, and Intelligence: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2025Review
- Semantic Scene Completion in Autonomous Driving: A Two-Stream Multi-Vehicle Collaboration Approach.Sensors (Basel, Switzerland) · 2024Article
- Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.Sensors (Basel, Switzerland) · 2024Article
- Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement Learning.Sensors (Basel, Switzerland) · 2024Article
- Enhanced Perception for Autonomous Vehicles at Obstructed Intersections: An Implementation of Vehicle to Infrastructure (V2I) Collaboration.Sensors (Basel, Switzerland) · 2024Article
- Collaborative Perception-The Missing Piece in Realizing Fully Autonomous Driving.Sensors (Basel, Switzerland) · 2023Article
- A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles.Sensors (Basel, Switzerland) · 2023Review
- Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review.Sensors (Basel, Switzerland) · 2022Review
- Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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Registered trials
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.