ReviewSensors (Basel, Switzerland)2022
Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: 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 9 papers.
What it found
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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
9 citing papers in PubMed.
- Mobile-Master-Vehicle-Based LiDAR Perception and Centralized Control of Sensor-Light Slave Vehicles.Sensors (Basel, Switzerland) · 2026Article
- Spatial Geometry Analysis of Roadside LiDAR for Improved Vehicle Clustering Accuracy.Sensors (Basel, Switzerland) · 2026Article
- Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection.Scientific reports · 2025Article
- Improving Vehicle Connectivity Through a Novel Self-Organizing Network Mechanism.Sensors (Basel, Switzerland) · 2025Article
- Improved YOLOv5 Network for High-Precision Three-Dimensional Positioning and Attitude Measurement of Container Spreaders in Automated Quayside Cranes.Sensors (Basel, Switzerland) · 2024Article
- Deep Neural Network-Based Phase-Modulated Continuous-Wave LiDAR.Sensors (Basel, Switzerland) · 2024Article
- Enhanced Perception for Autonomous Vehicles at Obstructed Intersections: An Implementation of Vehicle to Infrastructure (V2I) Collaboration.Sensors (Basel, Switzerland) · 2024Article
- DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device.Sensors (Basel, Switzerland) · 2023Article
- Research on an Adaptive Method for the Angle Calibration of Roadside LiDAR Point Clouds.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.
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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.