Understanding LiDAR Performance for Autonomous Vehicles Under Snowfall Conditions

Abstract

Light detection and ranging sensors (LiDARs) have become indispensable for autonomous vehicles because of their high detection accuracy and independence from lighting conditions. At present, although LiDARs with various beams have been developed, their performance is degraded to varying degrees in severe weather such as rain and snowfall. This restricts the reliable operation of autonomous vehicles in all weather and all scenes. To quantitatively evaluate the impact of snow on LiDAR performance, this paper creates and publishes a LiDAR point cloud dataset (MSP dataset) covering different LiDAR models, snowfall intensities, and test scenarios. Through theoretical and data-driven analysis, the influence of snow on the performance parameters of LiDAR, such as point cloud distribution, detection range, and sensing accuracy, is evaluated qualitatively and quantitatively. The experimental results show that the presence of snow induces a significant amount of noise in LiDAR systems. The level of noise varies with distance and height, following the Gamma and t location-scale distributions, respectively. During heavy snowfall, the recognition of vehicles and pedestrians by LiDARs is reduced by 50% and 20%, respectively. The target detection accuracy is reduced by about 9 percentage points, and the effective detection distance is reduced by 14-17m. The sensing performance of LiDAR will be significantly reduced within the range affected by snowflake noise. The research results of this paper provide important data and a theoretical basis for evaluating the performance of LiDAR-based autonomous driving systems during snowfall conditions and designing corresponding LiDAR sensing and processing methods.