Effect of Snow on Automotive LiDAR Perception Under Controlled Climatic Chamber Conditions

With the increasing deployment of autonomous and semi-autonomous road vehicles, Advanced Driver Assistance Systems (ADASs) rely heavily on multi-modal sensing technologies to ensure safe and reliable operation. Among these sensors, Light Detection and Ranging (LiDAR) provides high-resolution three-dimensional environmental perception but is particularly vulnerable to adverse weather conditions such as snowfall. Snowfall can degrade LiDAR performance through signal attenuation, backscattering, false detections, and sensor surface contamination, ultimately reducing visibility and detection reliability. In this study, an experimental investigation was conducted in a climatic chamber to systematically assess LiDAR performance degradation under controlled snowfall conditions. Key parameters influencing sensor behavior, including chamber air temperature, precipitation intensity, and sensor orientation, were isolated and examined. Chamber temperature was varied to generate snow characteristics representative of dry and wet snow, while precipitation intensity was controlled by adjusting snow gun flow rates. Sensor orientation was modified to evaluate its effect on perceived precipitation and snow accumulation. The experimental results confirm the initial hypothesis that snowfall intensity, snow physical properties, and sensor orientation exert a significant influence on LiDAR performance degradation. Increasing precipitation intensity significantly accelerates both 3D target detection loss and 2D visibility reduction, with polynomial regression revealing a non-linear degradation response. Inclined sensor orientations exhibited more rapid performance deterioration compared to a horizontal configuration. These findings provide valuable insights into LiDAR vulnerability in snowy environments and support the development of mitigation strategies to improve ADAS and autonomous vehicle operation in cold climates.