mlsanityData to 7 Oct 2026

Optimizing the Placement of Roadside LiDARs for Autonomous Driving

arXiv 2310.07247 ↗First posted 11 Oct 2023cs.CV cs.ROcited by 7

Not on the map. Why?

Abstract (arXiv)

Multi-agent cooperative perception is an increasingly popular topic in the field of autonomous driving, where roadside LiDARs play an essential role. However, how to optimize the placement of roadside LiDARs is a crucial but often overlooked problem. This paper proposes an approach to optimize the placement of roadside LiDARs by selecting optimized positions within the scene for better perception performance. To efficiently obtain the best combination of locations, a greedy algorithm based on perceptual gain is proposed, which selects the location that can maximize the perceptual gain sequentially. We define perceptual gain as the increased perceptual capability when a new LiDAR is placed. To obtain the perception capability, we propose a perception predictor that learns to evaluate LiDAR placement using only a single point cloud frame. A dataset named Roadside-Opt is created using the CARLA simulator to facilitate research on the roadside LiDAR placement problem.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

27 of 44 references.

References · 27

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  8. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
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15 more
  1. 2022Latency-Aware Collaborative Perceptioncited by 86
  2. 2022Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Taskcited by 79
  3. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  4. 2020Learning to Communicate and Correct Pose Errorscited by 46
  5. 2022Uncertainty Quantification of Collaborative Detection for Self-Drivingcited by 46
  6. 2018LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networkscited by 41
  7. 2018YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloudcited by 32
  8. 2022V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perceptioncited by 28
  9. 2020Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Cameracited by 22
  10. 2021Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Drivingcited by 22
  11. 2017Submodular Trajectory Optimization for Aerial 3D Scanningcited by 16
  12. 2022Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Librarycited by 14
  13. 2018Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approachcited by 12
  14. 2021Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Designcited by 5
  15. 2022Optimizing Fiducial Marker Placement for Improved Visual Localizationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 3.

7 papers.

  1. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
  2. 2025MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detectioncited by 3
  3. 2026SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Datacited by 1
  4. 2026RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perceptioncited by 1
  5. 2024UniDrive: Towards Universal Driving Perception Across Camera Configurationscited by 0
  6. 2025Which LiDAR scanning pattern is better for roadside perception: Repetitive or Non-repetitive?cited by 0
  7. 2026When the City Teaches the Car: Label-Free 3D Perception from Infrastructurecited by 0