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

Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technologies to provide perceptions and accurate distance measurements to 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 the self-driving features. However, navigating in adverse weather remains to be one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, which subsequently leads to performance degradation that poses 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 were 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 approach by employing 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 distributions, 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 a LiDAR is an optical type of sensor, its perception does not necessarily align with camera vision that is closer to human perception. Specifically, hydrophilic surfaces are found to show better advantages over hydrophobic surfaces for LiDAR sensor applications when driving in rain. Phenomenological models are presented to demonstrate the relationships between material properties, adherent raindrop dynamics, and LiDAR perceptions.