Spatially-Aware Reliability Modeling for BEV LiDAR 3D Vehicle Detection.

Bird's-eye-view (BEV) LiDAR detectors provide an efficient representation for real-time 3D perception, but their confidence scores may be imperfectly aligned with the localization quality of decoded 3D boxes. This score-localization mismatch can reduce the reliability of detection ranking, especially under strict IoU criteria and range-dependent LiDAR observations. To address this issue, we propose Spatially-Aware Quality Calibration (SAQC), a lightweight reliability-oriented scoring framework for BEV LiDAR 3D vehicle detection. SAQC estimates localization quality from coordinate-augmented local BEV feature patches around detected object centers, fuses the estimated quality with the raw detector score for quality-aware ranking, and applies post hoc sigmoid calibration with soft IoU targets for numerical quality alignment. Experiments on the KITTI Car validation split using an SFA3D-style center-based detector show that SAQC improves Moderate 3D AP from 89.13 to 89.57 at IoU = 0.7 and from 74.40 to 75.72 at IoU = 0.8. It also increases score-IoU Spearman correlation from 0.3685 to 0.4043 and reduces post-calibration Q-ECE from 0.0284 to 0.0183, while maintaining 103.6 FPS. These results indicate that local BEV spatial context can improve score-localization reliability without modifying decoded box geometry.