Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving
| Authors | Yuhan Zhou et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.06544 |
| Download | |
| Categories | cs.CV |
Abstract
Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal () 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 to , to , and from to , on three representative overlap regions, while detection on other overlapping camera pairs remains at the baseline even under stronger pruning. In AV2, - of labels are removed, and mAP{50} stays near the 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
Engineering Breakdown
Plain English
This paper addresses a critical but overlooked problem in autonomous vehicle datasets: data redundancy across multiple cameras and sensors. The authors analyze the nuScenes and Argoverse 2 datasets to measure how much redundant labeling exists when multiple cameras capture overlapping scenes, then demonstrate that selectively removing these duplicate labels has minimal impact on YOLOv8 object detection performance. The key finding is that you can significantly reduce annotation effort and dataset size by identifying and eliminating redundant labels from cameras with shared fields of view, without sacrificing model accuracy. This is important because AV companies spend enormous resources labeling multimodal sensor streams, and reducing redundancy could cut those costs substantially.
Core Technical Contribution
The core novelty is formalizing and measuring redundancy as a data quality problem in multisource, multimodal autonomous vehicle datasets—something the field has largely ignored while focusing on algorithm design. The authors develop a methodology to quantify label redundancy across camera views and modalities, then systematically evaluate how redundancy removal affects downstream detection performance. Unlike prior work that assumes all labeled data is equally valuable, this paper provides an empirical framework for identifying which labels are truly informative versus redundant duplicates. The practical insight is that significant label redundancy exists in standard AV datasets, creating an opportunity to optimize annotation efficiency without performance loss.
How It Works
The approach starts by analyzing the spatial overlap and temporal alignment between camera feeds in nuScenes and Argoverse 2—identifying which camera pairs share overlapping fields of view. For each overlapping region, the authors measure how much object label redundancy exists (e.g., the same vehicle labeled multiple times across cameras). They then create variants of the datasets with redundant labels progressively removed, targeting labels from cameras with high overlap first. These pruned datasets are used to train YOLOv8 object detectors, and performance is measured against the full dataset baseline to quantify the impact of redundancy removal. The key output is a redundancy profile showing which labels can be safely removed and how much annotation effort this saves, while maintaining detection accuracy within acceptable margins.
Production Impact
For AV teams managing annotation pipelines, this work directly translates to cost reduction in labeling workflows. If you're processing multimodal sensor data from 5-8 cameras per vehicle, you could potentially reduce redundant annotations by 20-40% (depending on overlap) without retraining detection models from scratch. In practice, this means prioritizing labels from non-overlapping or minimally-overlapping camera views, then using selective annotation for shared regions. The tradeoff is modest: you gain annotation efficiency and reduced storage, but lose some label diversity that might help with edge cases or out-of-distribution scenarios. For companies running continuous data pipelines (collecting thousands of hours of sensor data daily), even 20% reduction in annotation cost translates to significant budget savings and faster dataset iteration cycles.
Limitations and When Not to Use This
The paper's scope is limited to object detection (YOLOv8) on specific datasets—results may not generalize to other tasks like instance segmentation, panoptic segmentation, or semantic segmentation where label density and redundancy have different implications. The analysis assumes relatively static camera configurations; in real-world deployments with changing sensor mounts, calibration drift, or different vehicle platforms, redundancy patterns could shift. The paper doesn't address temporal redundancy across frames (consecutive frames in video with near-identical detections), only spatial redundancy across simultaneous camera views. Additionally, removing labels could hurt performance on difficult cases (occlusions, rain, night driving) that are rarer in certain camera views—the paper doesn't stratify results by scene difficulty or environmental conditions.
Research Context
This work sits at the intersection of data quality for autonomous driving and dataset efficiency research. It builds on prior work in AV perception (nuScenes, Argoverse benchmarks) and data curation but shifts focus from algorithm design to practical data engineering. The paper contributes to an emerging research direction around efficient annotation strategies and dataset optimization for multimodal learning. It opens up follow-up questions about optimal camera placement, selective annotation policies, and how redundancy interacts with domain shift and distribution mismatch in AV systems.
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