# A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

arXiv: https://arxiv.org/abs/1912.03251 · HTML: https://mlsanity.com/paper/1912.03251/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 30 of 32)

- [Robo3D: Towards Robust and Reliable 3D Perception against Corruptions](https://mlsanity.com/paper/2303.17597/) (2023)
- [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://mlsanity.com/paper/2203.15118/) (2022)
- [CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection](https://mlsanity.com/paper/2210.09267/) (2022)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds](https://mlsanity.com/paper/2209.07121/) (2022)
- [Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2407.02286/) (2024)
- [NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving](https://mlsanity.com/paper/2209.14499/) (2022)
- [SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2508.16408/) (2025)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Cross-spectral Gated-RGB Stereo Depth Estimation](https://mlsanity.com/paper/2405.12759/) (2024)
- [Robust 3D Object Detection in Cold Weather Conditions](https://mlsanity.com/paper/2205.11925/) (2022)
- [Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues](https://mlsanity.com/paper/2305.12955/) (2023)
- [Gated2Gated: Self-Supervised Depth Estimation from Gated Images](https://mlsanity.com/paper/2112.02416/) (2021)
- [A Benchmark for Spray from Nearby Cutting Vehicles](https://mlsanity.com/paper/2108.10800/) (2021)
- [Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D Sensing](https://mlsanity.com/paper/2406.07833/) (2024)
- [Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds](https://mlsanity.com/paper/2207.04908/) (2022)
- [Simulating Road Spray Effects in Automotive Lidar Sensor Models](https://mlsanity.com/paper/2212.08558/) (2022)
- [Gated Fields: Learning Scene Reconstruction from Gated Videos](https://mlsanity.com/paper/2405.19819/) (2024)
- [LED: Light Enhanced Depth Estimation at Night](https://mlsanity.com/paper/2409.08031/) (2024)
- [3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions](https://mlsanity.com/paper/2510.22436/) (2025)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults](https://mlsanity.com/paper/2209.01710/) (2022)
- [Multi-Echo Denoising in Adverse Weather](https://mlsanity.com/paper/2305.14008/) (2023)
- [Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor](https://mlsanity.com/paper/2502.06114/) (2025)
- [A Hybrid mmWave and Camera System for Long-Range Depth Imaging](https://mlsanity.com/paper/2106.07856/) (2021)
- [GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies](https://mlsanity.com/paper/2107.08948/) (2021)
- [Detecting the Anomalies in LiDAR Pointcloud](https://mlsanity.com/paper/2308.00187/) (2023)
- [SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions](https://mlsanity.com/paper/2407.04908/) (2024)
- [Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR](https://mlsanity.com/paper/2408.08555/) (2024)
- [Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation](https://mlsanity.com/paper/2603.01074/) (2026)

## Abstract (arXiv)

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data.