  Modern lane detection methods have achieved remarkable performances in
complex real-world scenarios, but many have issues maintaining real-time
efficiency, which is important for autonomous vehicles. In this work, we
propose LaneATT: an anchor-based deep lane detection model, which, akin to
other generic deep object detectors, uses the anchors for the feature pooling
step. Since lanes follow a regular pattern and are highly correlated, we
hypothesize that in some cases global information may be crucial to infer their
positions, especially in conditions such as occlusion, missing lane markers,
and others. Thus, this work proposes a novel anchor-based attention mechanism
that aggregates global information. The model was evaluated extensively on
three of the most widely used datasets in the literature. The results show that
our method outperforms the current state-of-the-art methods showing both higher
efficacy and efficiency. Moreover, an ablation study is performed along with a
discussion on efficiency trade-off options that are useful in practice.
