面向安全自动驾驶的大数据驱动3D目标检测中使用主动学习的原因、时机与方法：一项实证探索
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
AI总结：
针对自动驾驶3D目标检测的数据不平衡、冗余等问题，本文实证探索主动学习策略，验证熵查询在nuScenes数据集上相较随机采样的优势，可高效分配标注资源、缩小类别性能差距，适用于资源受限场景。
摘要：
自动驾驶数据集中3D目标检测的主动学习策略，或有助于解决数据不平衡、冗余以及高维数据带来的挑战。本文验证了熵查询（entropy querying）筛选信息丰富样本的有效性，旨在降低标注成本并提升模型性能。我们在nuScenes数据集上使用BEVFusion模型开展3D目标检测实验，将主动学习与随机采样进行对比，结果表明熵查询在多数情况下表现更优。该方法在缩小多数类与少数类之间的性能差距方面效果尤为显著。类别特异性分析显示，在有限数据预算下可实现标注资源的高效分配，凸显了筛选多样化且信息丰富的数据用于模型训练的重要性。研究结果表明，熵查询是一种颇具潜力的数据筛选策略，能够在资源受限环境下增强模型学习效果。
英文摘要：
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
