In order to improve the speed of lane line detection in autonomous driving, a method of feature extraction using convolutional neural network and classification network to realize the classification of virtual and solid lane lines is proposed. An efficient residual factorized ConvNet (ERFNet) is used to perform convolution operations and down sampling on images, the network adopts a bottleneck free one-dimensional convolution residual structure, utilizes vertical and horizontal one-dimensional convolution interpolation to enhance the generalization ability of nonlinear functions, obtains multi-scale contextual information based on variable fill ratios, to achieve feature extraction of images. After deconvolution and up sampling, the features are decoded and the image scale is restored, and finally the segmented image information is output. Compared to traditional semantic segmentation algorithms, this method can reduce a large number of feature parameters, enhance the learning ability of the model, and ensure detection accuracy while improving detection speed. The simulation experiments under conditions such as straight driving, turning, uphill, downhill, bumpy roads, and uneven lighting show that the detection accuracy of this method can reach 95.14%, and the detection speed is improved compared to mainstream algorithms.