CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection
Richard Beuck, John Nguyen, Sudip Dhakal
Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measurements far beyond both. We present CLOCs-R, an extension of the CLOCs candidate-level fusion framework that adds radar evidence to camera-LiDAR rescoring. Radar returns are accumulated over several sweeps, compensated for ego-motion, and associated with each pre-NMS 3D candidate through a range-dependent gate. Compact features summarizing support, Doppler speed and spread, range residual, and offset are then appended to every camera-LiDAR pair before a lightweight fusion network rescores the candidates. We first verify that our fusion implementation reproduces CLOCs on KITTI. We then evaluate on the daytime clear-weather subset of the aiMotive dataset, reporting distance-binned metrics out to 200 m with frozen SECOND and Cascade R-CNN detectors. Camera-LiDAR CLOCs improves on SECOND up to 50 m but degrades beyond it. A residual scoring variant, introduced post hoc, corrects SECOND's score instead of replacing it and recovers most of this loss. Radar consistently improves BEV AP at 30-50 m (0.857 to 0.870), but beyond 50 m its gain is small (+0.003 center-distance AP), within seed variation, and CLOCs-R with residual scoring is statistically tied with LiDAR-only SECOND. Our analysis attributes the limited long-range benefit to weak separation of true and false detections by radar features, scarce far-range training positives, poor camera evidence at distance, and missing LiDAR proposals that rescoring cannot recover. We report all as-designed and post hoc results side by side.
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