We propose UAD, a method for vision-based end-to-end autonomous driving
(E2EAD), achieving the best open-loop evaluation performance in nuScenes,
meanwhile showing robust closed-loop driving quality in CARLA. Our motivation
stems from the observation that current E2EAD models still mimic the modular
architecture in typical driving stacks, with carefully designed supervised
perception and prediction subtasks to provide environment information for
oriented planning. Although achieving groundbreaking progress, such design has
certain drawbacks: 1) preceding subtasks require massive high-quality 3D
annotations as supervision, posing a significant impediment to scaling the
training data; 2) each submodule entails substantial computation overhead in
both training and inference. To this end, we propose UAD, an E2EAD framework
with an unsupervised proxy to address all these issues. Firstly, we design a
novel Angular Perception Pretext to eliminate the annotation requirement. The
pretext models the driving scene by predicting the angular-wise spatial
objectness and temporal dynamics, without manual annotation. Secondly, a
self-supervised training strategy, which learns the consistency of the
predicted trajectories under different augment views, is proposed to enhance
the planning robustness in steering scenarios. Our UAD achieves 38.7% relative
improvements over UniAD on the average collision rate in nuScenes and surpasses
VAD for 41.32 points on the driving score in CARLA's Town05 Long benchmark.
Moreover, the proposed method only consumes 44.3% training resources of UniAD
and runs 3.4 times faster in inference. Our innovative design not only for the
first time demonstrates unarguable performance advantages over supervised
counterparts, but also enjoys unprecedented efficiency in data, training, and
inference. Code and models will be released at
this https URL

我们提出了 UAD，一种基于视觉的端到端自动驾驶 (E2EAD) 方法，在 nuScenes 中实现了最佳的开环评估性能，同时在 CARLA 中显示出强大的闭环驾驶质量。