Robust and efficient 3D perception is critical for autonomous vehicles operating in complex environments. Multi-sensor fusion, such as Camera+LiDAR, Camera+Radar, or all three modalities, significantly enhances scene understanding, However, most existing frameworks fuse data at a fixed stage, categorized as early fusion (raw data level), mid fusion (intermediate feature level), or late fusion (detection output level), neglecting semantic consistency across modalities. This static strategy may result in performance degradation or unnecessary computation under sensor misalignment or noise. In this work, we propose FDSNet (Feature Disagreement Score Network), a dynamic fusion framework that adaptively selects the fusion stage based on measured semantic consistency across sensor modalities. Each sensor stream (Camera, LiDAR, and Radar) independently extracts mid-level features, which are then transformed into a common Bird’s Eye View (BEV) representation, ensuring spatial alignment across modalities. To assess agreement, a Feature Disagreement Score (FDS) is computed at each BEV location by measuring statistical deviation across modality features. These local scores are aggregated into a global FDS value, which is compared against threshold to determine the fusion strategy. A low FDS, indicating strong semantic consistency across modalities, triggers mid-level fusion for computational efficiency, whereas a high FDS value activates late fusion to preserve detection robustness under cross-modal disagreement. We evaluate FDSNet on the nuScenes dataset across multiple configurations: Camera+Radar, Camera+LiDAR, and Camera+Radar+LiDAR. Experimental results demonstrate that FDSNet achieves consistent improvements over recent multimodal baselines, with gains of up to +3.0% in NDS and +2.6% in mAP on the validation set, and +2.1% in NDS and +1.6% in mAP on the test set, highlighting that dynamic stage selection provides both robustness and quantifiable advantages over static fusion strategies.