Real-time 3D object detection in occluded scenes is a core challenge for pure-vision autonomous driving perception. Existing dense Bird's Eye View (BEV) detection methods suffer from redundant backbone parameters, inflexible fixed sampling strategies, and coarse-grained temporal modeling, making it difficult to simultaneously satisfy detection accuracy and real-time latency requirements on embedded vehicle platforms. This paper proposes a lightweight BEV perception optimization framework for occluded scenes. An occlusion-aware adaptive voxel feature sampling module dynamically switches between fast ray projection and deformable attention paths according to scene complexity. A temporal grouping fusion module based on Res2Net principles performs grouped cross-frame fusion of consecutive BEV feature maps without introducing additional learnable parameters. A two-stage LiDAR-to-camera knowledge distillation scheme with a geometric compensation module transfers depth geometry knowledge during training while incurring zero inference overhead. Experiments on the nuScenes dataset demonstrate that the proposed method achieves 38.7% mAP and 51.3% NDS under the lightweight configuration, with an inference latency of 38.2 ms and 26.2 FPS on NVIDIA Tesla T4, ranking highest in NDS among comparable methods.