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.