RESAR-BEV: An Explainable Progressive Residual Autoregressive Approach for Camera-Radar Fusion in BEV Segmentation
This addresses perception challenges for autonomous driving systems, representing a strong incremental improvement over existing BEV segmentation methods.
The paper tackles the problem of multi-modal misalignment and sensor noise in Bird's-Eye-View semantic segmentation for autonomous driving by proposing RESAR-BEV, a progressive refinement framework that achieves state-of-the-art performance with 54.0% mIoU across 7 categories while maintaining real-time capability at 14.6 FPS.
Bird's-Eye-View (BEV) semantic segmentation provides comprehensive environmental perception for autonomous driving but suffers multi-modal misalignment and sensor noise. We propose RESAR-BEV, a progressive refinement framework that advances beyond single-step end-to-end approaches: (1) progressive refinement through residual autoregressive learning that decomposes BEV segmentation into interpretable coarse-to-fine stages via our Drive-Transformer and Modifier-Transformer residual prediction cascaded architecture, (2) robust BEV representation combining ground-proximity voxels with adaptive height offsets and dual-path voxel feature encoding (max+attention pooling) for efficient feature extraction, and (3) decoupled supervision with offline Ground Truth decomposition and online joint optimization to prevent overfitting while ensuring structural coherence. Experiments on nuScenes demonstrate RESAR-BEV achieves state-of-the-art performance with 54.0% mIoU across 7 essential driving-scene categories while maintaining real-time capability at 14.6 FPS. The framework exhibits robustness in challenging scenarios of long-range perception and adverse weather conditions.