Lane detection is a challenging task that requires predicting complex topology shapes of
lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a …

Modern deep-learning-based lane detection methods are successful in most scenarios but
struggling for lane lines with complex topologies. In this work, we propose CondLaneNet, a …

Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a
traffic sign with high-level semantics, whereas it owns the specific local pattern which needs …

Recently, lane detection has made great progress with the rapid development of deep
neural networks and autonomous driving. However, there exist three mainly problems …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problem of challenging scenarios and speed. Inspired by …

Lane detection is one of the most important tasks in self-driving. Due to various complex
scenarios (eg, severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problems of efficiency and challenging scenarios like …

Modern lane detection methods have achieved remarkable performances in complex real-
world scenarios, but many have issues maintaining real-time efficiency, which is important …

We present Laneformer, a conceptually simple yet powerful transformer-based architecture
tailored for lane detection that is a long-standing research topic for visual perception in …

Lane detection is to detect lanes on the road and provide the accurate location and shape of
each lane. It severs as one of the key techniques to enable modern assisted and …