车道线检测作为智能汽车视觉导航系统的核心技术，其性能直接影响到车辆的路径引导与转向控制，对提升交通安全性和导航效率具有重要意义。车道线图像中背景信息往往占据主导地位，特别是远距离车道线存在特征小、标记缺失或被遮挡等问题，同时伴随视觉上的宽度变化，导致远距离车道线比正常车道线更加难以识别。为解决这一问题，该文提出了结合增强空间感知的车道线检测方法。该方法首先针对车道线在图像中呈细长结构的特点，在主干网络中引入条形池化，以细化车道线信息的表示；然后将增强空间感知优化器（ESAO）和车道线多尺度聚合器（LMSA）相结合，以抑制无关背景和增强远距离车道线特征，进而提高车道线检测的准确性和稳定性；最后，通过全局和局部斜率一致性损失函数自适应调整车道线形状与位置，以保持预测车道线与地面真实情况之间的形状一致性。在2个车道线检测数据集TuSimple和CULane上的实验结果表明，该方法优于对比实验中最优秀的方法，在数据集TuSimple上的F₁分数和准确率分别提高0.58和0.19个百分点，在数据集CULane上的F₁@50提高1.14个百分点，特别是在远距离道路场景中表现的性能更加稳定。

Lane detection serves as a core technology for the visual navigation systems of intelligent vehicles. Its performance directly impacts a vehicle’s path guidance and steering control, which is of great significance for improving traffic safety and navigation efficiency. In lane images, background information often dominates, especially when distant lane markings exhibit challenges such as small feature size, absence, or occlusion, coupled with perspective-induced width variations. These issues make distant lane detection considerably more challenging than detecting nearby lanes. To address this problem, this paper proposed a lane detection method enhanced with spatial perception. Firstly, conside-ring the elongated morphology of lane markings in images, a strip-pooling module was incorporated into the backbone network to refine the representation of lane information. Furthermore, an Enhanced Spatial-Aware Optimizer (ESAO) was integrated with a Lane Multi-Scale Aggregator (LMSA) to suppress irrelevant background interference and enhance the features of distant lane markings, thereby improving the accuracy and robustness of lane detection. Finally, a global and local slope consistency loss function is designed to adaptively adjust the shape and position of lane lines, maintaining geometric consistency between the predicted lanes and the ground truth. Experimental evaluations conducted on the TuSimple and CULane benchmarks demonstrate that the proposed approach outperforms the state-of-the-art methods in comparative experiments. Specifically, it achieves improvements of 0.58 percentage points in F₁ score and 0.19 percen-tage points in accuracy on the TuSimple dataset, and 1.14 percentage points in F₁@50 on the CULane dataset. Notably, the method exhibits more stable performance, particularly in long-range road scenarios.