mlsanityData to 7 Oct 2026

Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library

arXiv 2211.15975 ↗First posted 29 Nov 2022cs.RO cs.CVcited by 14

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Abstract (arXiv)

Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR placement, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that when using the same number and type of LiDAR, the placement scheme optimized by our proposed method improves the average precision by 15%, compared with the conventional placement scheme in the standard lane scene. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance. Both the RLS Library and related code will be released at https://github.com/PJLab-ADG/PCSim.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

25 of 37 references.

References · 25

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  4. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  5. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  6. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  7. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  8. 2020V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Predictioncited by 292
  9. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  10. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  11. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 162
  12. 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
13 more
  1. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 95
  2. 2022Latency-Aware Collaborative Perceptioncited by 86
  3. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  4. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 72
  5. 2018Deep Generative Modeling of LiDAR Datacited by 70
  6. 2020Learning to Communicate and Correct Pose Errorscited by 46
  7. 2022Uncertainty Quantification of Collaborative Detection for Self-Drivingcited by 46
  8. 2018LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networkscited by 41
  9. 2018YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloudcited by 32
  10. 2019Semantic Understanding of Foggy Scenes with Purely Synthetic Datacited by 22
  11. 2020Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Cameracited by 22
  12. 2018Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approachcited by 12
  13. 2021Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Designcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 2; 2025: 2; 2026: 2.

14 papers.

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  1. 2023Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perceptioncited by 38
  2. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  3. 2023Collaborative Multi-Object Tracking with Conformal Uncertainty Propagationcited by 27
  4. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
  5. 2025Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Drivingcited by 14
  6. 2023ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulationcited by 13
  7. 2023Optimizing the Placement of Roadside LiDARs for Autonomous Drivingcited by 7
  8. 2023HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2Vcited by 6
  9. 2023Realistic Rainy Weather Simulation for LiDARs in CARLA Simulatorcited by 6
  10. 2025Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspectivecited by 5
  11. 2023TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matchingcited by 2
  12. 2026SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Datacited by 1
2 more
  1. 2026RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perceptioncited by 1
  2. 2024UniDrive: Towards Universal Driving Perception Across Camera Configurationscited by 0