分析自动驾驶多源数据冗余，发现删减冗余标签可提升检测性能。
Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving
- 通过建模多摄像头与图像-LiDAR数据的重叠区域，量化数据冗余度。
- 在nuScenes上删减冗余标签后，部分重叠区域mAP50从0.66升至0.70。
- 发现图像与激光雷达间存在显著冗余，适合关注数据质量的开发者参考。
下一代自动驾驶车辆依赖大量多源多模态（M²）数据实现实时决策。现实中，受环境与传感器限制，不同来源和模态的数据质量差异显著，但当前研究多聚焦算法设计而忽视数据质量分析。本文聚焦冗余这一基础但未被充分探索的数据质量问题，在nuScenes和Argoverse 2（AV2）数据集上建模并测量多源摄像头数据及图像-LiDAR多模态数据的冗余性，并评估移除冗余标注对YOLOv8目标检测任务的影响。实验表明，有选择地移除视场重叠摄像头的冗余图像标注可提升检测性能：在nuScenes中，三个典型重叠区域的mAP50分别从0.66升至0.70、0.64升至0.67、0.53升至0.55；其他重叠相机对即使强剪枝仍保持基线水平。在AV2中，4.1%–8.6%的标签被移除，mAP50维持在0.64基线附近。多模态分析进一步揭示图像与LiDAR间存在显著冗余。结果表明，冗余是可度量且可操作的数据质量因素，直接影响自动驾驶感知性能。本工作强调冗余作为数据质量核心要素的作用，推动以数据为中心的自动驾驶数据集评估与优化。
原文摘要 · Abstract (English)
Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to support real-time decision-making. In practice, data quality (DQ) varies across sources and modalities due to environmental conditions and sensor limitations, yet AV research has largely prioritized algorithm design over DQ analysis. This work focuses on redundancy as a fundamental but underexplored DQ issue in AV datasets. Using the nuScenes and Argoverse 2 (AV2) datasets, we model and measure redundancy in multisource camera data and multimodal image-LiDAR data, and evaluate how removing redundant labels affects the YOLOv8 object detection task. Experimental results show that selectively removing redundant multisource image object labels from cameras with shared fields of view improves detection. In nuScenes, mAP${50}$ gains from $0.66$ to $0.70$, $0.64$ to $0.67$, and from $0.53$ to $0.55$, on three representative overlap regions, while detection on other overlapping camera pairs remains at the baseline even under stronger pruning. In AV2, $4.1$-$8.6\%$ of labels are removed, and mAP${50}$ stays near the $0.64$ baseline. Multimodal analysis also reveals substantial redundancy between image and LiDAR data. These findings demonstrate that redundancy is a measurable and actionable DQ factor with direct implications for AV performance. This work highlights the role of redundancy as a data quality factor in AV perception and motivates a data-centric perspective for evaluating and improving AV datasets. Code, data, and implementation details are publicly available at: https://github.com/yhZHOU515/RedundancyAD
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