Transformer 是一种基于自注意力机制, 并行化处理数据的深度神经网络. 近几年基于
Transformer 的模型成为计算机视觉任务的重要研究方向. 针对目前国内基于Transformer …

In the context of autonomous vehicles on highways, one of the first and most important tasks
is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

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 …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

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 …

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

This paper presents a novel parametric curve-based method for lane detection in RGB
images. Unlike state-of-the-art segmentation-based and point detection-based methods that …

Lane detection, the process of identifying lane markings as approximated curves, is widely
used for lane departure warning and adaptive cruise control in autonomous vehicles. The …

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 …