  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.
