3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions

• URL: http://arxiv.org/abs/2510.22436v1
• Date: Sat, 25 Oct 2025 21:14:45 GMT
• Title: 3D Roadway Scene Object Detection with LIDARs in Snowfall Conditions
• Authors: Ghazal Farhani, Taufiq Rahman, Syed Mostaquim Ali, Andrew Liu, Mohamed Zaki, Dominique Charlebois, Benoit Anctil,
• Abstract summary: This study focuses on the performance of an automotive grade LiDAR in snowy conditions.<n>We investigate how the LiDAR signal attenuates with different snowfall rates and how snow particles near the source serve as small but efficient reflectors.
• Score: 2.1580846693824776
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Because 3D structure of a roadway environment can be characterized directly by a Light Detection and Ranging (LiDAR) sensors, they can be used to obtain exceptional situational awareness for assitive and autonomous driving systems. Although LiDARs demonstrate good performance in clean and clear weather conditions, their performance significantly deteriorates in adverse weather conditions such as those involving atmospheric precipitation. This may render perception capabilities of autonomous systems that use LiDAR data in learning based models to perform object detection and ranging ineffective. While efforts have been made to enhance the accuracy of these models, the extent of signal degradation under various weather conditions remains largely not quantified. In this study, we focus on the performance of an automotive grade LiDAR in snowy conditions in order to develop a physics-based model that examines failure modes of a LiDAR sensor. Specifically, we investigated how the LiDAR signal attenuates with different snowfall rates and how snow particles near the source serve as small but efficient reflectors. Utilizing our model, we transform data from clear conditions to simulate snowy scenarios, enabling a comparison of our synthetic data with actual snowy conditions. Furthermore, we employ this synthetic data, representative of different snowfall rates, to explore the impact on a pre-trained object detection model, assessing its performance under varying levels of snowfall

Related papers

• On the Assessment of Sensitivity of Autonomous Vehicle Perception [0.13858851827255522]
  The viability of automated driving is heavily dependent on the performance of perception systems.<n>We evaluate perception performance using predictive sensitivity quantification based on an ensemble of models.<n>A perception assessment criterion is developed based on an AV's stopping distance at a stop sign on varying road surfaces.
  arXiv  Detail & Related papers  (2026-01-30T21:06:05Z)
• Challenges of YOLO Series for Object Detection in Extremely Heavy Rain: CALRA Simulator based Synthetic Evaluation Dataset [0.0]
  Object detection by diverse sensors (e.g., LiDAR, radar, and camera) should be prioritized for autonomous vehicles. These sensors require to detect objects accurately and quickly in diverse weather conditions, but they tend to have challenges to consistently detect objects in bad weather conditions with rain, snow, or fog. In this study, based on experimentally obtained raindrop data from precipitation conditions, we constructed a novel dataset that could test diverse network model in various precipitation conditions.
  arXiv  Detail & Related papers  (2023-12-13T08:45:57Z)
• Robust Monocular Depth Estimation under Challenging Conditions [81.57697198031975]
  State-of-the-art monocular depth estimation approaches are highly unreliable under challenging illumination and weather conditions. We tackle these safety-critical issues with md4all: a simple and effective solution that works reliably under both adverse and ideal conditions.
  arXiv  Detail & Related papers  (2023-08-18T17:59:01Z)
• Survey on LiDAR Perception in Adverse Weather Conditions [6.317642241067219]
  The active LiDAR sensor is able to create an accurate 3D representation of a scene. The LiDAR's performance change under adverse weather conditions like fog, snow or rain. We address topics such as the availability of appropriate data, raw point cloud processing and denoising, robust perception algorithms and sensor fusion to mitigate adverse weather induced shortcomings.
  arXiv  Detail & Related papers  (2023-04-13T07:45:23Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• LiDAR Snowfall Simulation for Robust 3D Object Detection [116.10039516404743]
  We propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds. Our method samples snow particles in 2D space for each LiDAR line and uses the induced geometry to modify the measurement for each LiDAR beam. We use our simulation to generate partially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall.
  arXiv  Detail & Related papers  (2022-03-28T21:48:26Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
  We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors. LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs. It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
  arXiv  Detail & Related papers  (2020-03-13T06:17:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.