RawRadarFusion: A Raw mmWave Radar Data-Fused Multimodal Object Detection Framework for Autonomous Driving
Automotive millimeter-wave (mmWave) radar is crucial for all-weather autonomous driving, making radar-camera fusion a highly promising perception solution. Conventional fusion frameworks rely on sparse radar point clouds generated via constant false alarm rate (CFAR) filtering, suffering from severe information loss. While recent raw-tensor-based approaches successfully bypass CFAR, they introduce prohibitive computational burdens and struggle with cross-modal spatial misalignments. To address these challenges, we propose RawRadarFusion, a highly efficient multimodal 3-D object detection paradigm that fundamentally shifts away from heavy tensor operations. First, we compress raw 4-D radar tensors into a lightweight motion saliency index (MSI) pseudoimage. This velocity-weighted representation preserves essential macrokinematics and elevation cues while drastically reducing data dimensionality. Second, we introduce the deformable columnwise cross-modal attention (DCCA) module, which leverages geometrically constrained learnable offsets to achieve precise spatial alignment between radar and camera features, effectively mitigating calibration drifts without the overhead of global attention. Extensive experiments on the K-Radar dataset demonstrate that RawRadarFusion not only achieves highly competitive accuracy against dense-tensor state-of-the-art models but also achieves an accuracy–efficiency tradeoff while exhibiting strong robustness against calibration error. Furthermore, migration experiments on a self-built multimodal dataset confirm its strong generalization capability, establishing it as a practical and efficient new method for industrial deployment.