# Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study

arXiv: https://arxiv.org/abs/2309.10504 · HTML: https://mlsanity.com/paper/2309.10504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection](https://mlsanity.com/paper/2510.24688/) (2025)
- [Depth Matters: Multimodal RGB-D Perception for Robust Autonomous Agents](https://mlsanity.com/paper/2503.16711/) (2025)
- [Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors](https://mlsanity.com/paper/2603.27273/) (2026)

## Abstract (arXiv)

Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions and the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, RADAR, and Depth-Camera. Based on accurate experimental evaluation the papers findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving.