# Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design

arXiv: https://arxiv.org/abs/2104.06615 · HTML: https://mlsanity.com/paper/2104.06615/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [LIBRE: The Multiple 3D LiDAR Dataset](https://mlsanity.com/paper/2003.06129/) (2020)
- [Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach](https://mlsanity.com/paper/1809.05845/) (2018)
- [An Optimal LiDAR Configuration Approach for Self-Driving Cars](https://mlsanity.com/paper/1805.07843/) (2018)
- [Sensor placement by maximal projection on minimum eigenspace for linear inverse problems](https://mlsanity.com/paper/1506.00747/) (2015)

## Built on by (most cited first, 5 of 5)

- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [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)
- [ZOPP: A Framework of Zero-shot Offboard Panoptic Perception for Autonomous Driving](https://mlsanity.com/paper/2411.05311/) (2024)

## Abstract (arXiv)

Sensor configuration, including the sensor selections and their installation locations, serves a crucial role in autonomous driving. A well-designed sensor configuration significantly improves the performance upper bound of the perception system. However, as leveraging multiple sensors is becoming the mainstream setting, existing methods mainly focusing on single-sensor configuration problems are hardly utilized in practice. To tackle these issues, we propose a novel method based on conditional entropy in Bayesian theory to evaluate the sensor configurations containing both cameras and LiDARs. Correspondingly, an evaluation metric, perception entropy, is introduced to measure the difference between two configurations, which considers both the perception algorithm performance and the selections of the sensors. To the best of our knowledge, this is the first method to tackle the multi-sensor configuration problem for autonomous vehicles. The simulation results, extensive comparisons, and analysis all demonstrate the superior performance of our proposed approach.