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
this https URL

本文提出了一种主动数据收集策略来改善自主驾驶算法中需要的数据集质量问题，实验结果表明该策略可以显著降低标注成本和数据集大小，提高数据集质量，从而提高自主驾驶系统的性能。