工程设计学报2026，Vol.33Issue(3)：334-344,11.DOI:10.3785/j.issn.1006-754X.2026.06.114
基于GNN-Transformer模型的车道线检测方法
Lane line detection method based on GNN-Transformer model
摘要
Abstract
Lane line detection is a crucial part of autonomous driving technology.To address the problem of high false detection rate,as well as the difficulty in balancing frame rate and accuracy in the autonomous driving scenarios,an end-to-end GNN-Transformer detection framework was developed,in which graph neural network(GNN)was used to enhance the local geometric consistency of lane lines and a Transformer encoder-decoder was employed to complete the global dependency modeling and lane line prediction.In addition,learnable positional encoding was adopted and an optimized curve fitting strategy was introduced to improve the model's adaptability to complex scenarios.The proposed lane line detection method was experimentally verified on the TuSimple dataset,CULane dataset and CARLA simulator.Experimental results on the TuSimple dataset showed that the proposed method achieved a false detection rate of 0.019 2,which was reduced by up to 89%compared with other six methods,including ORANet.Meanwhile,the frame rate remained at 110 frames per second,indicating that the method achieved high detection accuracy,stability and real-time performance.Furthermore,the model was deployed on an RTRC4pro intelligent vehicle,thereby further evaluating the engineering application potential of the proposed method.The research results can provide strong support for the online perception of lane lines and its engineering applications in real-vehicle scenarios.关键词
图神经网络/Transformer/车道线拟合/注意力机制Key words
graph neural network/Transformer/lane line fitting/attention mechanism分类
交通工程引用本文复制引用
贾子厚,罗佳,郑利锋,张艳岗,刘正阳,刘奔飞..基于GNN-Transformer模型的车道线检测方法[J].工程设计学报,2026,33(3):334-344,11.基金项目
中北大学校企合作项目(2412000072HX) （2412000072HX）
