A learning method using an input image having one or more lanes, comprising steps of: instructing, by a learning device, if the input image is acquired, a convolutional neural network module to apply at least one convolution operation to the input image to thereby generate a feature map and then apply at least one deconvolution operation to the feature map to thereby generate each of segmentation scores of each of pixels on the input image; instructing, by the learning device, the CNN module to apply at least one Softmax operation to each of the segmentation scores to thereby generate each of Softmax scores; and instructing, by the learning device, the CNN module to apply a multinomial logistic loss operation to the Softmax scores to thereby generate a Softmax loss and to apply a pixel embedding operation to the Softmax scores to thereby generate an embedding loss which causes a learning of the CNN module to increase each of inter-lane differences among respective averages of the Softmax scores of the respective lanes and decrease each of intra-lane variances among the Softmax scores of the respective lanes, and then to learn at least one parameter of the CNN module through backpropagation by using the Softmax loss and the embedding loss, wherein the multinomial logistic loss is calculated by using an equation: softmax_loss = − 1 s ∑ i s log P i l, and wherein s denotes the number of the pixels included in the one input image, I denotes a one-hot-encoding vector indicating to which cluster an i-th pixel belongs on its corresponding ground truth label, and P(i) denotes each of Softmax scores corresponding to each of the pixels, wherein the embedding loss is calculated by using a formula: 1 NC ∑ c = 1 NC 1 NC C ∑ p = 1 NC c max 0 ‖ x p − μ c ‖ − maxDev 2 + 1 NC × NC − 1 ∑ c 1 ∈ C, c 2 ∈ C max 0 minDist − ‖ μ c 1 − μ c 2 ‖ 2 minDist 2, and wherein, assuming that the input image includes a plurality of clusters having the lanes and one or more background parts, NC denotes the number of the clusters including the lanes and the background parts on the input image, NC c denotes the number of pixels in each of the clusters, μ c denotes each average of Softmax scores corresponding to respective pixels included in each of the clusters, maxDev denotes a maximum value among variances calculated by referring to the Softmax scores and the μ c corresponding to the respective pixels, and minDist denotes a minimum value among differences between μ c s corresponding to different clusters. instructing, by a learning device, if the input image is acquired, a convolutional neural network module to apply at least one convolution operation to the input image to thereby generate a feature map and then apply at least one deconvolution operation to the feature map to thereby generate each of segmentation scores of each of pixels on the input image; instructing, by the learning device, the CNN module to apply at least one Softmax operation to each of the segmentation scores to thereby generate each of Softmax scores; and instructing, by the learning device, the CNN module to apply a multinomial logistic loss operation to the Softmax scores to thereby generate a Softmax loss and to apply a pixel embedding operation to the Softmax scores to thereby generate an embedding loss which causes a learning of the CNN module to increase each of inter-lane differences among respective averages of the Softmax scores of the respective lanes and decrease each of intra-lane variances among the Softmax scores of the respective lanes, and then to learn at least one parameter of the CNN module through backpropagation by using the Softmax loss and the embedding loss, wherein the multinomial logistic loss is calculated by using an equation: softmax_loss = − 1 s ∑ i s log P i l, and wherein s denotes the number of the pixels included in the one input image, I denotes a one-hot-encoding vector indicating to which cluster an i-th pixel belongs on its corresponding ground truth label, and P(i) denotes each of Softmax scores corresponding to each of the pixels, wherein the embedding loss is calculated by using a formula: 1 NC ∑ c = 1 NC 1 NC C ∑ p = 1 NC c max 0 ‖ x p − μ c ‖ − maxDev 2 + 1 NC × NC − 1 ∑ c 1 ∈ C, c 2 ∈ C max 0 minDist − ‖ μ c 1 − μ c 2 ‖ 2 minDist 2, and wherein, assuming that the input image includes a plurality of clusters having the lanes and one or more background parts, NC denotes the number of the clusters including the lanes and the background parts on the input image, NC c denotes the number of pixels in each of the clusters, μ c denotes each average of Softmax scores corresponding to respective pixels included in each of the clusters, maxDev denotes a maximum value among variances calculated by referring to the Softmax scores and the μ c corresponding to the respective pixels, and minDist denotes a minimum value among differences between μ c s corresponding to different clusters.