Active Learning (AL) has remained relatively unexplored for LiDAR perception tasks in autonomous driving datasets. In this study we evaluate Bayesian active learning methods applied to the task of dataset distillation or core subset selection (subset with near equivalent performance as full dataset). We also study the effect of application of data augmentation (DA) within Bayesian AL based dataset distillation. We perform these experiments on the full Semantic-KITTI dataset. We extend our study over our existing work only on 1/4th of the same dataset. Addition of DA and BALD have a negative impact over the labeling efficiency and thus the capacity to distill datasets. We demonstrate key issues in designing a functional AL framework and finally conclude with a review of challenges in real world active learning.

翻译：主动学习(AL) 相对来说,对于自动驱动数据集中的 LiDAR 感知任务,我们尚未探索。在本研究中,我们评估了贝叶西亚用于数据集蒸馏或核心子集选择任务的积极学习方法(近似等效的子集作为完整的数据集)。我们还研究了在巴伊西亚的AL 基数据集蒸馏中应用数据增强(DA)的影响。我们在全语义-KITTI数据集上进行了这些实验。我们只将研究扩展至同一数据集的四分之一的现有工作。添加DA和BALD对标签效率产生了负面影响,从而影响了蒸馏数据集的能力。我们在设计功能AL 框架时展示了关键问题,并在最后对现实世界积极学习中的挑战进行了审查。