Getting an accurate estimation of three-dimensional position of the driveable lane is crucial for autonomous driving. In this work, we introduce a novel attention module called Dual Attention (DA) which enables the model to perform robustly and accurately under complicated enviromental conditions. More specifically, the attention mechanism adopts a two-pathway correlated attention method to produce additional features and aggregate globle information. We demonstrate the effectiveness of our method by following and extending recently proposed state-of-the-art 3D lane marking detection methods. Moreover, we use a novel linear-interpolation loss to precisely fit the lane marking. Extensive conducted experiments demonstrate that our methods outperform baseline methods on Apollo synthetic 3D dataset.