When Does Score Fusion Help? Conformally Certified Out-of-Distribution Detection for Camera and LiDAR Sensors

Camera and LiDAR sensors in safety-critical autonomous systems suffer undetected distributional shifts that silently corrupt downstream perception. Out-of-distribution (OOD) detection is the established sensor-data-integrity primitive, but no single post hoc detector covers every shift type, and existing detectors lack guarantees on their false-positive rate (FPR). This paper asks when calibrated score fusion helps and provides a distribution-free finite-sample FPR certificate. Four post hoc scores—Maximum Softmax Probability (MSP), Energy, Mahalanobis distance and k-nearest-neighbour (KNN) distance—are calibrated to p-values by the empirical cumulative distribution function (ECDF) and combined by Fisher’s method or cross-backbone z-score averaging, then wrapped in a conformal predictor with Hoeffding-based Probably Approximately Correct (PAC) bounds. On the full-split PUG camera benchmark (215,040 images), uniform same-backbone p-value fusion does not beat the best single detector (Mahalanobis); the gain comes from cross-backbone diversity: a z-score average of Mahalanobis distances over ResNet-50 and frozen DINOv2 reaches a mean area-under-the-ROC-curve (AUROC) of 0.9258 (+0.0199), rising to 0.9292 (+0.0233) with added spectral and dropout signals (DeLong p<10−9). On the nuScenes LiDAR sensor (256,873 frames), uniform fusion yields only a small, calibration-sensitive gain over the best single detector (MSP), so the substantial fusion gain is confined to cross-backbone averaging on the camera. The distribution-free PAC certificate, by contrast, transfers across both sensors with margins below 1.5% (0.96% camera, 0.25% LiDAR), giving evidence usable in ISO 26262 and EASA CoDANN safety cases.