HVA-Fusion:Hierarchical Velocity-Aware 4D Radar-LiDAR Fusion for Robust 3D Object Detection

3D object detection is a core task in autonomous driving per-ception, with mainstream methods primarily based on LiDAR. However,its robustness deteriorates significantly in adverse weather conditions.While 4D radar offers all-weather robustness and unique Doppler veloc-ity, it is hindered by its inherent sparsity and noise interference. There-fore, fusing LiDAR and 4D radar is highly promising. However, due to thesignificant differences in semantics and density between the data of thesetwo modalities, single feature space fusion cannot fully leverage the ad-vantages of each modality and the complementarity between modalities.To address this, we propose a two-stage fusion detection framework—HVA-Fusion. Specifically, motion velocity is first estimated through aMotion Velocity Estimation and Encoding module to enhance dynamicperception. Then, in the pillar feature space, a Cross-Modal Progres-sive Adaptation module is introduced for local feature alignment andfusion (Stage-I fusion) to enrich representations and mitigate semanticdifferences between modalities. After that, to alleviate the sparsity of4D radar point cloud, a Radar Cross-Section (RCS) Guided GaussianGeneration module is developed to densify the feature representations.Subsequently, in the Bird’s Eye View (BEV) space, the enhanced multi-modal features are adaptively fused through a Multi-Scale Bi-DirectionalDeformable Attention Gate module (Stage-II fusion) to address featuremisalignment and sensor degradation caused by adverse weather. Exten-sive experiments demonstrate that HVA-Fusion achieves highly compet-itive performance across the K-Radar, VoD, and TJ4DRadSet datasets,while exhibiting robustness in adverse weather conditions.