← 研踪 PaperTrailarXiv:2611.09121 · v1
CoMET: Tracking the Shared Pose Error of Roadside Messages for Asynchronous Vehicle-Infrastructure Cooperative 3D Detection
首次提交 2026-11-06 15:59
版本更新 2026-11-06 15:59
雷达观测 2026-11-09 11:19
摘要
A roadside sensor can detect vehicles hidden from the ego vehicle, but a pose error shifts every detection in the same message. We study asynchronous vehicle-infrastructure cooperative 3D detection on V2X-Seq using only information available at decision time, without ground truth, future frames, or per-pair calibration offsets at inference. Objects seen by both sensors reveal the shared translation, but even at zero added delay, 38% of validation messages have no matched pair. We propose CoMET (Common-Mode Error Tracking), which estimates this translation over time. At each message arrival, CoMET matches vehicle and roadside detections, estimates the offset from mutually nearest pairs, and updates a Kalman filter for that sender. When no pair matches, the filter carries its estimate forward. The correction applies to all roadside boxes, including those unseen by the ego vehicle. The estimator has no learned parameters; a separate frozen head ranks the merged detections. Across four delays from 0 to 400 ms on 21 validation sequences, CoMET averages 66.63 bird's-eye-view AP at IoU 0.7, compared with 63.18 for the same late fusion using uncorrected online poses and 62.96 for the best intermediate-fusion baseline. Across five detectors from three architectures, CoMET gains 2.2 to 3.5 AP points over online poses and 0.3 to 0.6 over estimating the offset from the current message alone. We find that the benchmark uses a zero offset as a placeholder in 28% of validation messages and that its offset search reaches the boundary in 45% of the rest. These offsets affect the labels for objects seen only from the roadside. Under two alternative label references, CoMET improves on uncorrected online poses in every evaluated population. Retraining intermediate fusion with CoMET poses and corrected labels improves detection on all three architectures tested, with statistically significant gains on two.
作者备注：25 pages, 4 figures. Code and experimental results archived at https://doi.org/10.5281/zenodo.23999418
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v1 · 2026-11-06 15:59 · 最新观测版本
CoMET: Tracking the Shared Pose Error of Roadside Messages for Asynchronous Vehicle-Infrastructure Cooperative 3D Detection
A roadside sensor can detect vehicles hidden from the ego vehicle, but a pose error shifts every detection in the same message. We study asynchronous vehicle-infrastructure cooperative 3D detection on V2X-Seq using only information available at decision time, without ground truth, future frames, or per-pair calibration offsets at inference. Objects seen by both sensors reveal the shared translation, but even at zero added delay, 38% of validation messages have no matched pair. We propose CoMET (Common-Mode Error Tracking), which estimates this translation over time. At each message arrival, CoMET matches vehicle and roadside detections, estimates the offset from mutually nearest pairs, and updates a Kalman filter for that sender. When no pair matches, the filter carries its estimate forward. The correction applies to all roadside boxes, including those unseen by the ego vehicle. The estimator has no learned parameters; a separate frozen head ranks the merged detections. Across four delays from 0 to 400 ms on 21 validation sequences, CoMET averages 66.63 bird's-eye-view AP at IoU 0.7, compared with 63.18 for the same late fusion using uncorrected online poses and 62.96 for the best intermediate-fusion baseline. Across five detectors from three architectures, CoMET gains 2.2 to 3.5 AP points over online poses and 0.3 to 0.6 over estimating the offset from the current message alone. We find that the benchmark uses a zero offset as a placeholder in 28% of validation messages and that its offset search reaches the boundary in 45% of the rest. These offsets affect the labels for objects seen only from the roadside. Under two alternative label references, CoMET improves on uncorrected online poses in every evaluated population. Retraining intermediate fusion with CoMET poses and corrected labels improves detection on all three architectures tested, with statistically significant gains on two.
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