  The search for predictive models that generalize to the long tail of sensor
inputs is the central difficulty when developing data-driven models for
autonomous vehicles. In this paper, we use lane detection to study modeling and
training techniques that yield better performance on real world test drives. On
the modeling side, we introduce a novel fully convolutional model of lane
detection that learns to decode lane structures instead of delegating structure
inference to post-processing. In contrast to previous works, our convolutional
decoder is able to represent an arbitrary number of lanes per image, preserves
the polyline representation of lanes without reducing lanes to polynomials, and
draws lanes iteratively without requiring the computational and temporal
complexity of recurrent neural networks. Because our model includes an estimate
of the joint distribution of neighboring pixels belonging to the same lane, our
formulation includes a natural and computationally cheap definition of
uncertainty. On the training side, we demonstrate a simple yet effective
approach to adapt the model to new environments using unsupervised style
transfer. By training FastDraw to make predictions of lane structure that are
invariant to low-level stylistic differences between images, we achieve strong
performance at test time in weather and lighting conditions that deviate
substantially from those of the annotated datasets that are publicly available.
We quantitatively evaluate our approach on the CVPR 2017 Tusimple lane marking
challenge, difficult CULane datasets, and a small labeled dataset of our own
and achieve competitive accuracy while running at 90 FPS.
