Bird's-Eye-View (BEV) perception is fundamental for autonomous driving, providing a unified spatial representation that fuses surrounding-view images for downstream tasks like semantic segmentation, 3D object detection, and motion prediction. Current end-to-end BEV frameworks treat image-to-BEV transformation as a black box, lacking explicit 3D geometric understanding and often yielding suboptimal performance. This paper introduces Splat2BEV, a Gaussian Splatting-assisted framework that learns BEV feature representations combining semantic richness with geometric precision. By leveraging 3D Gaussian Splatting for reconstruction, the method explicitly incorporates 3D geometry awareness into the BEV perception pipeline, addressing the limitations of traditional end-to-end approaches.