A learning method for learning a CNN capable of detecting one or more lanes, comprising steps of: (a) a learning device instructing one or more convolutional layers to generate one or more feature maps by applying one or more convolution operations to an input image selected from at least one image data set; (b) the learning device instructing an FC layer to generate an estimated result vector of cluster ID classifications of the lanes in the input image by feeding a feature map from the one or more feature maps outputted from the convolutional layers into the FC layer; and (c) the learning device instructing a loss layer to generate at least one classification loss by referring to the estimated result vector of the cluster ID classifications and a cluster ID GT vector, and backpropagate the classification loss, to thereby optimize device parameters of the CNN; wherein K cluster groups are assigned to function parameters of a lane modeling function by clustering all function parameters of the lane modeling function from every image of the at least one image data set, said function parameters being calculated by using pixel information of coordinates of center points of the lanes in each image of the at least one image data set, wherein each of K cluster IDs is assigned to each of the K cluster groups, wherein the cluster ID GT vector is a K+1 dimensional vector, and (i) on condition that the lanes are determined as present in the input image, if there is a specific lane, having a specific cluster ID C k among the K cluster IDs, among the lanes in the input image, then a k-th dimension of the cluster ID GT vector is filled with 1, other dimensions of the cluster ID GT vector which respectively correspond to other cluster IDs, among the cluster IDs, not in the input image are filled with 0, and a (K+1)-th dimension of the cluster ID GT vector is filled with 0, and (ii) if no lanes are determined as present in the input image, a first dimension to a K-th dimension of the cluster ID GT vector are filled with 0, and the (K+1)-th dimension of the cluster ID GT vector is filled with 1, wherein k is an integer ranging from 1 to K, and wherein, at the step of (c), the estimated result vector of the cluster ID classifications is a K+1 dimensional vector. (a) a learning device instructing one or more convolutional layers to generate one or more feature maps by applying one or more convolution operations to an input image selected from at least one image data set; (b) the learning device instructing an FC layer to generate an estimated result vector of cluster ID classifications of the lanes in the input image by feeding a feature map from the one or more feature maps outputted from the convolutional layers into the FC layer; and (c) the learning device instructing a loss layer to generate at least one classification loss by referring to the estimated result vector of the cluster ID classifications and a cluster ID GT vector, and backpropagate the classification loss, to thereby optimize device parameters of the CNN; wherein K cluster groups are assigned to function parameters of a lane modeling function by clustering all function parameters of the lane modeling function from every image of the at least one image data set, said function parameters being calculated by using pixel information of coordinates of center points of the lanes in each image of the at least one image data set, wherein each of K cluster IDs is assigned to each of the K cluster groups, wherein the cluster ID GT vector is a K+1 dimensional vector, and (i) on condition that the lanes are determined as present in the input image, if there is a specific lane, having a specific cluster ID C k among the K cluster IDs, among the lanes in the input image, then a k-th dimension of the cluster ID GT vector is filled with 1, other dimensions of the cluster ID GT vector which respectively correspond to other cluster IDs, among the cluster IDs, not in the input image are filled with 0, and a (K+1)-th dimension of the cluster ID GT vector is filled with 0, and (ii) if no lanes are determined as present in the input image, a first dimension to a K-th dimension of the cluster ID GT vector are filled with 0, and the (K+1)-th dimension of the cluster ID GT vector is filled with 1, wherein k is an integer ranging from 1 to K, and wherein, at the step of (c), the estimated result vector of the cluster ID classifications is a K+1 dimensional vector.