Human drivers produce a vast amount of data which could, in principle, be
used to improve autonomous driving systems. Unfortunately, seemingly
straightforward approaches for creating end-to-end driving models that map
sensor data directly into driving actions are problematic in terms of
interpretability, and typically have significant difficulty dealing with
spurious correlations. Alternatively, we propose to use this kind of
action-based driving data for learning representations. Our experiments show
that an affordance-based driving model pre-trained with this approach can
leverage a relatively small amount of weakly annotated imagery and outperform
pure end-to-end driving models, while being more interpretable. Further, we
demonstrate how this strategy outperforms previous methods based on learning
inverse dynamics models as well as other methods based on heavy human
supervision (ImageNet).

该研究探讨了利用人类驾驶产生的数据对自动驾驶系统进行改进的方法，提出了利用基于行动的驾驶数据进行学习表示的模型，该模型在弱注释图像方面表现出色，并且优于纯粹的端到端驾驶模型，同时更具有可解释性。