Driving in the Rain: Evaluating How Surface Material Properties Affect LiDAR Perception in Autonomous Driving

<div class="section abstract"><div class="htmlview paragraph">Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this paper reports a fundamental study that employs a previously developed novel wind tunnel testing methodology to investigate and quantify the effects of stress factors affecting LiDAR perception. It was found that soiling characteristics, such as raindrop size distribution and droplet impact kinematics, and material properties, such as surface roughness and wettability, all play critical roles in influencing LiDAR performance to different extents. The results suggest that although LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision, which is closer to human perception. Specifically, hydrophilic surfaces show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. The overall relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions are summarized.</div></div>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-03-31
DOI
https://doi.org/10.4271/2025-01-8016
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.80
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article

Driving in the Rain: Evaluating How Surface Material Properties Affect LiDAR Perception in Autonomous Driving

Alina Baltazar, Langis Roy, Wing Yi Pao, Martin Agelin‐Chaab et al.
1 citations
SAE technical papers on CD-ROM/SAE technical paper series
Remote Sensing and LiDAR Applications
0.80
article

Driving in the Rain: Evaluating How Surface Material Properties Affect LiDAR Perception in Autonomous Driving

Alina Baltazar, Langis Roy, Wing Yi Pao, Martin Agelin‐Chaab, Julian Knutzen, Klaus Muenker, Long Li, Anirban Chakraborty, John Komar
article en
1 citations

Abstract

<div class="section abstract"><div class="htmlview paragraph">Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technology to provide perceptions and accurate distance measurements of obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid self-driving features. However, navigating adverse weather remains one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, leading to performance degradation that can pose safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies have been able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable testing. Therefore, this paper reports a fundamental study that employs a previously developed novel wind tunnel testing methodology to investigate and quantify the effects of stress factors affecting LiDAR perception. It was found that soiling characteristics, such as raindrop size distribution and droplet impact kinematics, and material properties, such as surface roughness and wettability, all play critical roles in influencing LiDAR performance to different extents. The results suggest that although LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision, which is closer to human perception. Specifically, hydrophilic surfaces show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. The overall relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions are summarized.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Magna International (Germany) (DE), Ontario Tech University (CA), Magna Exteriors (Czechia) (CZ), Abiola Ajimobi Technical University Ibadan (NG)
Openalex Percentile: Top 31%
Remote Sensing and LiDAR Applications
0.80
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