# Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

arXiv: https://arxiv.org/abs/1809.05845 · HTML: https://mlsanity.com/paper/1809.05845/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [Is Your LiDAR Placement Optimized for 3D Scene Understanding?](https://mlsanity.com/paper/2403.17009/) (2024)
- [Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving](https://mlsanity.com/paper/2105.00373/) (2021)
- [Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library](https://mlsanity.com/paper/2211.15975/) (2022)
- [Optimizing the Placement of Roadside LiDARs for Autonomous Driving](https://mlsanity.com/paper/2310.07247/) (2023)
- [Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design](https://mlsanity.com/paper/2104.06615/) (2021)
- [Optimal Placement of Roadside Infrastructure Sensors towards Safer Autonomous Vehicle Deployments](https://mlsanity.com/paper/2110.01251/) (2021)
- [Robustness Certification of Visual Perception Models via Camera Motion Smoothing](https://mlsanity.com/paper/2210.04625/) (2022)
- [Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations](https://mlsanity.com/paper/2309.13150/) (2023)
- [UniDrive: Towards Universal Driving Perception Across Camera Configurations](https://mlsanity.com/paper/2410.13864/) (2024)
- [Task-Driven Implicit Representations for Automated Design of LiDAR Systems](https://mlsanity.com/paper/2505.22344/) (2025)
- [Understanding Cross-Rig Generalization in Automotive Perception: a Multi-Rig Benchmark and Rig Variation Metrics](https://mlsanity.com/paper/2606.27554/) (2026)
- [A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies](https://mlsanity.com/paper/2609.00718/) (2026)

## 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.