  Self-driving vehicles must perceive and predict the future positions of
nearby actors in order to avoid collisions and drive safely. A learned deep
learning module is often responsible for this task, requiring large-scale,
high-quality training datasets. As data collection is often significantly
cheaper than labeling in this domain, the decision of which subset of examples
to label can have a profound impact on model performance. Active learning
techniques, which leverage the state of the current model to iteratively select
examples for labeling, offer a promising solution to this problem. However,
despite the appeal of this approach, there has been little scientific analysis
of active learning approaches for the perception and prediction (P&P) problem.
In this work, we study active learning techniques for P&P and find that the
traditional active learning formulation is ill-suited for the P&P setting. We
thus introduce generalizations that ensure that our approach is both cost-aware
and allows for fine-grained selection of examples through partially labeled
scenes. Our experiments on a real-world, large-scale self-driving dataset
suggest that fine-grained selection can improve the performance across
perception, prediction, and downstream planning tasks.
