Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
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Abstract (arXiv)
Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D views and precise distance measures under highly uncertain driving conditions. Its practical implementation, however, remains costly. This paper investigates the optimal LiDAR configuration problem to achieve utility maximization. We use the perception area and non-detectable subspace to construct the design procedure as solving a min-max optimization problem and propose a bio-inspired measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost function representing the notion of the size of the non-detectable subspaces of a given configuration. We then adopt a cuboid-based approach to show that the proposed VSR-based measure is a well-suited proxy for object detection rate. It is found that the Artificial Bee Colony evolutionary algorithm yields a tractable cost function computation. Our experiments highlight the effectiveness of our proposed VSR measure in terms of cost-effectiveness configuration as well as providing insightful analyses that can improve the design of AV systems.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2018.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 2; 2025: 1; 2026: 2.
12 papers.
- 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 25
- 2021Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Drivingcited by 22
- 2022Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Librarycited by 14
- 2023Optimizing the Placement of Roadside LiDARs for Autonomous Drivingcited by 7
- 2021Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Designcited by 5
- 2021Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deploymentscited by 3
- 2022Robustness Certification of Visual Perception Models via Camera Motion Smoothingcited by 3
- 2023Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbationscited by 2
- 2024UniDrive: Towards Universal Driving Perception Across Camera Configurationscited by 0
- 2025Task-Driven Implicit Representations for Automated Design of LiDAR Systemscited by 0
- 2026Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metricscited by 0
- 2026A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policiescited by 0
- 2026A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies1 Sep 2026
- 2026Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics25 Jun 2026
- 2025Task-Driven Implicit Representations for Automated Design of LiDAR Systems28 May 2025
- 2024UniDrive: Towards Universal Driving Perception Across Camera Configurations17 Oct 2024
- 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?25 Mar 2024
- 2023Optimizing the Placement of Roadside LiDARs for Autonomous Driving11 Oct 2023
- 2023Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations22 Sep 2023
- 2022Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library29 Nov 2022
- 2022Robustness Certification of Visual Perception Models via Camera Motion Smoothing4 Oct 2022
- 2021Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments4 Oct 2021
- 2021Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving2 May 2021
- 2021Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design14 Apr 2021