Accurate lane line information is crucial for path planning and decision-making modules in autonomous driving systems. To achieve high-precision lane detection, this paper leverages the inherent characteristics of lane lines—being slender and structurally continuous—as prior knowledge, proposing a lane-prior guided multi-scale lane detection network. The network employs ResNet-18 as the backbone feature extraction structure. Gated fusion modules are introduced at different stages to effectively suppress redundant information propagation and enhance cross-layer information interaction efficiency. Addressing the slender, continuous, and spatially extensive nature of lane lines, a large kernel attention mechanism is incorporated into the feature pyramid network (FPN), significantly expanding the receptive field and strengthening the network’s perception capability for lane lines across different scales. In the detection head, lane priors are combined with global features, effectively resolving the challenge of accurately identifying lane lines when relying solely on local pixels. Furthermore, an auxiliary segmentation branch is designed, utilizing a cross-entropy loss function to optimize the parameter update process and enhance the model’s expressive power for fine-grained features. Experiments conducted on the public TuSimple and CULane datasets demonstrate the effectiveness of the proposed method: it achieves 96.96% accuracy on the TuSimple dataset and an F1-score of 80.1% on the CULane dataset. The proposed method enables high-precision lane detection in complex scenarios and exhibits strong competitiveness among existing lane detection approaches.