AI RESEARCH
Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving
arXiv CS.LG
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ArXi:2604.04797v1 Announce Type: cross Accurate 3D object detection for autonomous driving requires complementary sensors. Cameras provide dense semantics but unreliable depth, while millimeter-wave radar offers precise range and velocity measurements with sparse geometry. We propose MMF-BEV, a radar-camera BEV fusion framework that leverages deformable attention for cross-modal feature alignment on the View-of-Delft (VoD) 4D radar dataset.