  This paper focuses on two crucial issues in domain-adaptive lane detection,
i.e., how to effectively learn discriminative features and transfer knowledge
across domains. Existing lane detection methods usually exploit a pixel-wise
cross-entropy loss to train detection models. However, the loss ignores the
difference in feature representation among lanes, which leads to inefficient
feature learning. On the other hand, cross-domain context dependency crucial
for transferring knowledge across domains remains unexplored in existing lane
detection methods. This paper proposes a method of Domain-Adaptive lane
detection via Contextual Contrast and Aggregation (DACCA), consisting of two
key components, i.e., cross-domain contrastive loss and domain-level feature
aggregation, to realize domain-adaptive lane detection. The former can
effectively differentiate feature representations among categories by taking
domain-level features as positive samples. The latter fuses the domain-level
and pixel-level features to strengthen cross-domain context dependency.
Extensive experiments show that DACCA significantly improves the detection
model's performance and outperforms existing unsupervised domain adaptive lane
detection methods on six datasets, especially achieving the best performance
when transferring from CULane to Tusimple (92.10% accuracy), Tusimple to CULane
(41.9% F1 score), OpenLane to CULane (43.0% F1 score), and CULane to OpenLane
(27.6% F1 score).
