Aggregate global features into separable hierarchical lane detection transformer.

Lane detection is one of the key functions to ensure the safe driving of autonomous vehicles, and it is a challenging task. In real driving scenarios, external factors inevitably interfere with the lane detection system, such as missing lane markings, harsh weather conditions, and vehicle occlusion. To enhance the accuracy and detection speed of lane detection in complex road environments, this paper proposes an end-to-end lane detection model with a pure Transformer architecture, which exhibits excellent detection performance in complex road scenes. Firstly, a separable lane multi-head attention mechanism based on window self-attention is proposed. This mechanism can establish the attention relationship between each window faster and more effectively, reducing the computational cost and improving the detection speed. Then, an extended and overlapping strategy is designed, which solves the problem of insufficient information interaction between two adjacent windows of the standard multi-head attention mechanism, thereby obtaining more global information and effectively improving the detection accuracy in complex road environments. Finally, experiments are carried out on four data sets. The experimental results indicate that the proposed method is superior to the existing state of the arts method in terms of both effectiveness and efficiency.