The previous studies showed that rainy and snowy weather can reduce the quality of LiDAR data. In rainy and snowy weather, laser beams of LiDAR were often blocked by raindrops or snowflakes, which was called weather occlusion. The vehicle detection with weather occlusion is a challenge. When the traditional density-based spatial clustering of applications with noise (DBSCAN) was used for vehicle clustering, the data processing showed that the false detection rate of the conventional DBSCAN under the snowy weather was high. This paper aims to present the characteristics of roadside LiDAR data in snowy and rainy days and improve the accuracy of vehicle detection during challenging weather conditions. A revised DBSCAN method named 3D-SDBSCAN is raised up to distinguish vehicle points and snowflakes in the LiDAR data. Adaptive parameters were applied in the revised DBSCAN method to detect vehicles with different distances from the roadside LiDAR sensor. The performance of the proposed method and the conventional DBSCAN algorithm were compared using the data collected under rainy and snowy conditions. The results showed that the 3D-SDBSCAN algorithm could overcome weather occlusion issue better than the conventional one.