The spatiotemporal misalignment and heterogeneous latency issues in vehicle-infrastructure cooperative 3D detection severely compromise the reliability of perception in autonomous driving systems. Existing methods suffer from rigid fusion paradigms that neglect dynamic object movements and in-discriminate feature fusion strategies causing conflicts in overlapping regions. To overcome these limitations, we propose HDFN, featuring three innovations: (1) Deformable alignment modules adaptively synchronize multi-scale features via learnable offset prediction, (2) Hierarchical fusion gates with channel-wise attention dynamically resolve cross-modal conflicts, and (3) Dual-latency adaptation strategies optimize model robustness for both low-latency and extreme-delay scenarios. Extensive experiments on V2X-Seq-SPD and DAIR-V2X datasets demonstrate HDFN’s superiority: achieving 4% higher mAP in low-latency environments and sustaining 3.5% robustness gains at 500ms delays compared to baselines, with only 10–15% communication overhead increase. Our framework advances cooperative perception by systematically balancing geometric fidelity and real-world latency constraints, offering theoretical insights into deformable fusion and practical solutions for safe autonomous navigation.