Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving

2026-03-06

AI Summary

This work addresses the long-overlooked redundancy in multi-source, multi-modal data for autonomous driving, which adversely impacts perception performance. For the first time, redundancy is formalized as a quantifiable data quality factor. The study systematically analyzes redundancy among multiple cameras and between image–LiDAR modalities in the nuScenes and Argoverse 2 datasets, proposing a method to identify redundant labels based on field-of-view overlap and cross-modal consistency. Experiments within the YOLOv8 object detection framework demonstrate that removing 4.1–8.6% of redundant labels maintains stable performance while improving mAP50 by up to 0.04 in specific regions of nuScenes. These findings validate the efficacy of redundancy-aware data curation and advance a data-centric paradigm in autonomous driving research.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

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

Problem

• redundancy
• data quality
• multisource multimodal data
• autonomous driving
• object detection

Innovation

• redundancy
• multisource multimodal data
• data quality
• autonomous driving
• object detection