Autonomous navigation faces significant challenges due to the limited field of view of sensors and occlusions caused by people, which can create obstructed regions. To address this, this paper proposes a dynamic crowd occlusion inference algorithm based on single-wire LiDAR, designed to integrate multimodal sensor data through occlusion inference in order to improve detection and navigation capabilities. The algorithm employs a pedestrian angle grid for interactive insights, a two-stage batch-normalized variational self-encoder to compress visible pedestrian dynamics and obstacle information into a one-dimensional representation, and a LiDAR point cloud map along with environmental background data to efficiently predict occlusion regions. Experimental results show that the algorithm achieves comparable navigation performance to that of a fully observable environment by estimating pedestrians in occluded areas. The success rate remains above 91% without the need to retrain the network for varying participant numbers. Additionally, this algorithm has been successfully applied to a wheeled mobile platform in real-world scenarios.