Li Mengyang, Chen Qi, Ge Zekun, Tao Fazhan, Wang Zhikai
College of Physics & Electronic Information, Luoyang Normal University, Luoyang, 471934, China.
School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China.
Sci Rep. 2025 Jan 22;15(1):2804. doi: 10.1038/s41598-025-86894-z.
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.
车道检测是确保自动驾驶车辆安全行驶的关键功能之一，也是一项具有挑战性的任务。在实际驾驶场景中，外部因素不可避免地会干扰车道检测系统，如车道标记缺失、恶劣天气条件和车辆遮挡。为了提高复杂道路环境下车道检测的准确性和检测速度，本文提出了一种具有纯Transformer架构的端到端车道检测模型，该模型在复杂道路场景中表现出优异的检测性能。首先，提出了一种基于窗口自注意力的可分离车道多头注意力机制。该机制可以更快、更有效地建立每个窗口之间的注意力关系，降低计算成本，提高检测速度。然后，设计了一种扩展和重叠策略，解决了标准多头注意力机制两个相邻窗口之间信息交互不足的问题，从而获得更多全局信息，有效提高复杂道路环境下的检测精度。最后，在四个数据集上进行了实验。实验结果表明，所提方法在有效性和效率方面均优于现有最先进方法。
