  We focus on bridging domain discrepancy in lane detection among different
scenarios to greatly reduce extra annotation and re-training costs for
autonomous driving. Critical factors hinder the performance improvement of
cross-domain lane detection that conventional methods only focus on pixel-wise
loss while ignoring shape and position priors of lanes. To address the issue,
we propose the Multi-level Domain Adaptation (MLDA) framework, a new
perspective to handle cross-domain lane detection at three complementary
semantic levels of pixel, instance and category. Specifically, at pixel level,
we propose to apply cross-class confidence constraints in self-training to
tackle the imbalanced confidence distribution of lane and background. At
instance level, we go beyond pixels to treat segmented lanes as instances and
facilitate discriminative features in target domain with triplet learning,
which effectively rebuilds the semantic context of lanes and contributes to
alleviating the feature confusion. At category level, we propose an adaptive
inter-domain embedding module to utilize the position prior of lanes during
adaptation. In two challenging datasets, ie TuSimple and CULane, our approach
improves lane detection performance by a large margin with gains of 8.8% on
accuracy and 7.4% on F1-score respectively, compared with state-of-the-art
domain adaptation algorithms.
