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Autonomous driving powered by deep learning requires large-scale, high-quality training data from diverse driving environments to operate effectively worldwide. However, collecting and annotating such data is costly and time-consuming. To address this challenge, active learning methods have been explored to select the most informative data samples for training. Nevertheless, most existing methods focus on 2D tasks and do not fully exploit the value of unlabeled data. In this paper, we propose a semi-supervised active learning approach for 3D object detection tasks that leverages the potential of collected data and reduces annotation costs. Our method considers the 3D consistency of bounding box predictions in both semi-supervised and active learning processes, thereby improving the performance of point cloud-based 3D object detection models. Our framework specifically utilizes self-supervision to …