Confidence-Calibrated Camera–LiDAR Fusion under Sensor Shift: Synchronized-Pair Association, Anomaly Detection, and Proposal-Tracklet Continuity
Keywords:
Camera–Lidar Fusion, Confidence Calibration, Sensor Shift, Synchronization, Anomaly Detection, Sensor-Pair Association, Tracklet Continuity, Autonomous Inland NavigationAbstract
Camera–LiDAR fusion can fail when sensor quality, synchronization, or extrinsic calibration departs from the conditions used to construct the fusion rule. This study evaluates whether confidence calibration makes those failures more visible and reduces overconfident association decisions. Experiments used the DAVE inland-waterway sequence, comprising 128 synchronized 1280 × 720 images, 128 LiDAR point clouds containing 5,719,139 XYZ points, and 781 scored two-dimensional privacy proposals. A chronological 64/32/32-frame train/calibration/test split prevented future frames from entering model fitting. Pair association distinguished synchronized camera–LiDAR frames from frames offset by 1, 2, 4, 8, or 16 s. Image-only, LiDAR-only, early summary-fusion, late evidence-rule, learned-fusion, and confidence-gated models were compared. Held-out discrimination was near chance: learned fusion obtained AUROC 0.4971 and confidence-gated fusion obtained 0.4865. Relative probability quality nevertheless improved on this block. The Platt-calibrated, confidence-gated pipeline reduced Brier score from 0.1768 to 0.1571 and expected calibration error from 0.1743 to 0.0968 relative to the uncalibrated learned model; paired frame-block bootstrap differences were −0.0197 (95% interval −0.0218 to −0.0177) and −0.0730 (−0.0827 to −0.0465), respectively. The fixed severe blur, camera-noise, LiDAR-dropout, and modality-loss conditions were perfectly separated, whereas temporal offsets and fixed extrinsic perturbations remained difficult. Because DAVE provides neither track identities nor manually verified object boxes, temporal experiments report continuity, forward–reverse agreement, edge retention, and spurious-link proxies rather than MOTA, IDF1, or HOTA. JPDA-style affinity produced the highest clean link rate, whereas confidence gating gave the best edge-retention and spurious-link proxies under bounding-box jitter. Calibration can therefore improve the meaning of confidence without creating discriminative evidence absent from the sequence.
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