RIGS: Radar-Informed Gaussian Splatting for Uncertainty-Aware 3D Occupancy and Motion Prediction

3D occupancy prediction provides dense semantic scene rep-resentations for autonomous driving, yet camera-only methods sufferfrom occlusion and adverse weather, while existing fusion approachestreat 4D radar as a generic point cloud and neglect its distinct physicalmeasurements. We propose RIGS, a radar-informed Gaussian splattingframework that systematically exploits 4D radar across three stages ofthe pipeline. In scene modeling, we extract range and power from the 4Dradar tensor for depth initialization and fuse them with images throughimage–radar–Gaussian tri-modal cross-attention. In occupancy refine-ment, we introduce evidential ellipsoidal BKI with anisotropic kernelsand use RCS power as physical existence evidence for adaptive false-positive filtering, yielding uncertainty-aware predictions with Dirichletepistemic and semantic uncertainty. In velocity estimation, we proposetemporal displacement-guided Doppler de-aliasing to recover high-speedradial velocities and complement tangential components, supervised bya multi-level loss over sparse radar points, dense voxels, and connectedcomponents. Experiments on K-Radar show that RIGS achieves stronglong-range occupancy among camera–radar methods with uncertainty-aware refinement and competitive velocity estimation, validated by sys-tematic incremental ablations.