A learning method of a CNN configured to detect one or more lanes using a lane model, comprising steps of: (a) a learning device acquiring information on the lanes from at least one image data set, wherein the information on the lanes are represented by respective sets of coordinates of center points of the lanes; (b) the learning device calculating one or more function parameters of a lane modeling function of each of the lanes by using the coordinates of center points of the lanes, wherein each lane modeling function is used for representing each lane; (c) the learning device performing processes of classifying the function parameters into K cluster groups by using a clustering algorithm, assigning each of one or more cluster IDs to each of the cluster groups, and generating a cluster ID Ground Truth, GT, vector representing GT information on probabilities of being the cluster IDs corresponding to types of the lanes; (d) the learning device learning device parameters of the CNN configured to estimate at least one of the cluster IDs corresponding to at least one of the lanes in an input image acquired from the at least one image data set; wherein the step of (d) includes steps of: the learning device instructing one or more convolutional layers to generate one or more feature maps by applying one or more convolution operations to the input image; the learning device instructing an FC layer to generate a result vector of cluster ID classifications of the lanes in the input image by feeding a specific feature map outputted from the convolutional layers into the FC layer; and the learning device instructing a loss layer to generate at least one classification loss by referring to the result vector of the cluster ID classifications and the cluster ID GT vector, and backpropagate the classification loss, to thereby optimize the device parameters of the CNN, 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, the result vector of the cluster ID classifications is a K+1 dimensional vector. (a) a learning device acquiring information on the lanes from at least one image data set, wherein the information on the lanes are represented by respective sets of coordinates of center points of the lanes; (b) the learning device calculating one or more function parameters of a lane modeling function of each of the lanes by using the coordinates of center points of the lanes, wherein each lane modeling function is used for representing each lane; (c) the learning device performing processes of classifying the function parameters into K cluster groups by using a clustering algorithm, assigning each of one or more cluster IDs to each of the cluster groups, and generating a cluster ID Ground Truth, GT, vector representing GT information on probabilities of being the cluster IDs corresponding to types of the lanes; (d) the learning device learning device parameters of the CNN configured to estimate at least one of the cluster IDs corresponding to at least one of the lanes in an input image acquired from the at least one image data set; the learning device instructing one or more convolutional layers to generate one or more feature maps by applying one or more convolution operations to the input image; the learning device instructing an FC layer to generate a result vector of cluster ID classifications of the lanes in the input image by feeding a specific feature map outputted from the convolutional layers into the FC layer; and the learning device instructing a loss layer to generate at least one classification loss by referring to the result vector of the cluster ID classifications and the cluster ID GT vector, and backpropagate the classification loss, to thereby optimize the device parameters of the CNN, 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, the result vector of the cluster ID classifications is a K+1 dimensional vector.