ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous
  Driving
Han Lu Xiaosong Jia Yichen Xie Wenlong Liao Xiaokang Yang Junchi Yan
End-to-end differentiable learning for autonomous driving (AD) has recently
become a prominent paradigm. One main bottleneck lies in its voracious appetite
for high-quality labeled data e.g. 3D bounding boxes and semantic segmentation,
which are notoriously expensive to manually annotate. The difficulty is further
pronounced due to the prominent fact that the behaviors within samples in AD
often suffer from long tailed distribution. In other words, a large part of
collected data can be trivial (e.g. simply driving forward in a straight road)
and only a few cases are safety-critical. In this paper, we explore a
practically important yet under-explored problem about how to achieve sample
and label efficiency for end-to-end AD. Specifically, we design a
planning-oriented active learning method which progressively annotates part of
collected raw data according to the proposed diversity and usefulness criteria
for planning routes. Empirically, we show that our planning-oriented approach
could outperform general active learning methods by a large margin. Notably,
our method achieves comparable performance with state-of-the-art end-to-end AD
methods - by using only 30% nuScenes data. We hope our work could inspire
future works to explore end-to-end AD from a data-centric perspective in
addition to methodology efforts.