Abstract. Lane detection is playing an indispensable role in advanced driver assistance systems. The existing approaches for lane detection can be categorized as lane area segmentation and lane boundary detection. Most of these methods abandon a great quantity of complementary information, such as geometric priors, when exploiting the lane area and the lane boundaries alternatively. In this paper, we establish a multiple-task learning framework to segment lane areas and detect lane boundaries simultaneously. The main contributions of the proposed framework are highlighted in two facets: (1) We put forward a multiple-task learning framework with mutually interlinked sub-structures between lane segmentation and lane boundary detection to improve overall performance. (2) A novel loss function is proposed with two geometric constraints considered, as assumed that the lane boundary is predicted as the outer contour of the lane area while the lane area is predicted as the area integration result within the lane boundary lines. With an end-to-end training process, these improvements extremely enhance the robustness and accuracy of our approach on several metrics. The proposed framework is evaluated on KITTI dataset, CULane dataset and RVD dataset. Compared with the state of the arts, our approach achieves the best performance on the metrics and a robust detection in varied traffic scenes