# Weather Influence and Classification with Automotive Lidar Sensors

arXiv: https://arxiv.org/abs/1906.07675 · HTML: https://mlsanity.com/paper/1906.07675/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 24 of 24)

- [Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2108.05249/) (2021)
- [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://mlsanity.com/paper/2203.15118/) (2022)
- [3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds](https://mlsanity.com/paper/2304.00690/) (2023)
- [LidarCLIP or: How I Learned to Talk to Point Clouds](https://mlsanity.com/paper/2212.06858/) (2022)
- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds](https://mlsanity.com/paper/2209.07121/) (2022)
- [V2X-DGW: Domain Generalization for Multi-agent Perception under Adverse Weather Conditions](https://mlsanity.com/paper/2403.11371/) (2024)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator](https://mlsanity.com/paper/2312.12772/) (2023)
- [STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy](https://mlsanity.com/paper/2309.11006/) (2023)
- [Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges](https://mlsanity.com/paper/2502.02692/) (2025)
- [Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds](https://mlsanity.com/paper/2207.04908/) (2022)
- [Verifiable Obstacle Detection](https://mlsanity.com/paper/2208.14403/) (2022)
- [Probabilistic Rainfall Estimation from Automotive Lidar](https://mlsanity.com/paper/2104.11467/) (2021)
- [Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults](https://mlsanity.com/paper/2209.01710/) (2022)
- [Where Am I Now? Dynamically Finding Optimal Sensor States to Minimize Localization Uncertainty for a Perception-Denied Rover](https://mlsanity.com/paper/2211.16721/) (2022)
- [Multi-Echo Denoising in Adverse Weather](https://mlsanity.com/paper/2305.14008/) (2023)
- [Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors](https://mlsanity.com/paper/2405.12736/) (2024)
- [Detecting the Anomalies in LiDAR Pointcloud](https://mlsanity.com/paper/2308.00187/) (2023)
- [Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR](https://mlsanity.com/paper/2408.08555/) (2024)
- [FDCT: Frequency-Aware Decomposition and Cross-Modal Token-Alignment for Multi-Sensor Target Classification](https://mlsanity.com/paper/2503.09873/) (2025)
- [Adaptive Augmentation-Aware Latent Learning for Robust LiDAR Semantic Segmentation](https://mlsanity.com/paper/2603.01074/) (2026)
- [From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests](https://mlsanity.com/paper/2606.11989/) (2026)

## Abstract (arXiv)

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.