<![CDATA[Late fusion remains attractive for multi-sensor 3D perception because it preserves independent sensor pipelines, enables modular upgrades, and supports rigorous ablation experiments. This paper presents an uncertainty-aware late-fusion framework that combines per-modality confidence calibration with learning a fusion rule. We conduct full experimental evaluations on a PandaSet-style LiDAR+camera subset comprising 10 multi-frame sequences and 2,200 synchronized frames, with 49,549 annotated 3D objects across the Car, Pedestrian, and Cyclist classes. The framework calibrates LiDAR and camera confidence using temperature scaling and isotonic regression, estimates uncertainty-conditioned localization variance, and fuses associated candidates using multiple rules (max, mean, product/odds, and Dempster-Shafer) as well as a learned fusion rule (logistic regression trained on association features). On the test split, isotonic calibration reduces LiDAR Expected Calibration Error from 0.260 to 0.006 and Negative Log-Likelihood from 0.410 to 0.110, and it similarly improves camera confidence quality. Although mean Average Precision (mAP) remains similar to a LiDAR-only baseline in this controlled setting, calibrated late fusion provides substantially better decision reliability at fixed confidence thresholds and maintains conservative high-precision behavior under camera dropout. These results support an engineering conclusion: confidence calibration is the highest-leverage upgrade for late fusion in safety-critical stacks, and fusion rule choice can be tuned to downstream risk preferences.]]>
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