  Autonomous driving algorithms rely heavily on learning-based models, which
require large datasets for training. However, there is often a large amount of
redundant information in these datasets, while collecting and processing these
datasets can be time-consuming and expensive. To address this issue, this paper
proposes the concept of an active data-collecting strategy. For high-quality
data, increasing the collection density can improve the overall quality of the
dataset, ultimately achieving similar or even better results than the original
dataset with lower labeling costs and smaller dataset sizes. In this paper, we
design experiments to verify the quality of the collected dataset and to
demonstrate this strategy can significantly reduce labeling costs and dataset
size while improving the overall quality of the dataset, leading to better
performance of autonomous driving systems. The source code implementing the
proposed approach is publicly available on
https://github.com/Th1nkMore/carla_dataset_tools.
