Modeling and Measuring Redundancy in Multisource Multimodal Data for Autonomous Driving

ArXi:2603.06544v1 Announce Type: new Next-generation autonomous vehicles (AVs) rely on large volumes of multisource and multimodal ($M^2$) data to 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.