Accurate and comprehensive recognition of dynamic and static objects is essential for the safety and efficiency of autonomous vehicles. However, deep learning-based semantic segmentation, which extracts semantic information from LiDAR point clouds, often struggles to maintain high accuracy due to the complexity of distinguishing target objects from nearby entities the point cloud data. This study presents a method of updating Semantic Dynamic Occupancy Grid Map (SDOGM), which simultaneously incorporates an object’s semantic and dynamic information into a unified grid map. For the robust estimation of the object’s semantic information occupying the grid cell, a combination rule is proposed based on Dempster-Shafer evidence theory. Additionally, the dynamic states of objects occupying the grid cell are updated through a particle filter-based method. This approach effectively distinguishes closely positioned objects by clustering grids according to class-specific characteristics.We evaluated the performance of speed estimation and object recognition using the urban nuScenes dataset. The results demonstrate that the proposed method recognizes a variety of surrounding objects, estimate their velocities, in complex urban environments.