A method for training a main Convolutional Neural Network to be used for performing an autonomous driving, by using at least one virtual image and at least one style-transformed real image, which has been transformed from one real image to an image whose style corresponds to a style of an image of a prescribed virtual world, comprising steps of: (a) acquiring, by a learning device, at least part of one or more first training images corresponding to a virtual driving of a virtual vehicle in the prescribed virtual world; and (b) performing, by the learning device, as a first training process, processes of (i) instructing a main convolutional layer and an output layer in the main CNN to generate one or more pieces of first estimated autonomous driving source information which is reference information to be used for supporting the autonomous driving by referring to the first training images, (ii) instructing a main loss layer in the main CNN to (ii-1) generate one or more first main losses by referring to the first estimated autonomous driving source information and its corresponding first Ground-Truth autonomous driving source information and (ii-2) perform backpropagation by using the first main losses, to thereby learn at least part of parameters of the main CNN, and as a second training process, processes of (i) instructing a supporting CNN to generate one or more second training images, each of which being generated by referring to one first base image selected among the first training images and one second base image corresponding to a real driving of a real vehicle in a real world, (ii) instructing the main convolutional layer and the output layer in the main CNN to generate one or more pieces of second estimated autonomous driving source information by referring to the second training images, (iii) instructing the main loss layer in the main CNN to (iii-1) generate one or more second main losses by referring to the second estimated autonomous driving source information and its corresponding second Ground-Truth autonomous driving source information and (iii-2) perform backpropagation by using the second main losses, to thereby learn at least part of parameters of the main CNN. (a) acquiring, by a learning device, at least part of one or more first training images corresponding to a virtual driving of a virtual vehicle in the prescribed virtual world; and (b) performing, by the learning device, as a first training process, processes of (i) instructing a main convolutional layer and an output layer in the main CNN to generate one or more pieces of first estimated autonomous driving source information which is reference information to be used for supporting the autonomous driving by referring to the first training images, (ii) instructing a main loss layer in the main CNN to (ii-1) generate one or more first main losses by referring to the first estimated autonomous driving source information and its corresponding first Ground-Truth autonomous driving source information and (ii-2) perform backpropagation by using the first main losses, to thereby learn at least part of parameters of the main CNN, and as a second training process, processes of (i) instructing a supporting CNN to generate one or more second training images, each of which being generated by referring to one first base image selected among the first training images and one second base image corresponding to a real driving of a real vehicle in a real world, (ii) instructing the main convolutional layer and the output layer in the main CNN to generate one or more pieces of second estimated autonomous driving source information by referring to the second training images, (iii) instructing the main loss layer in the main CNN to (iii-1) generate one or more second main losses by referring to the second estimated autonomous driving source information and its corresponding second Ground-Truth autonomous driving source information and (iii-2) perform backpropagation by using the second main losses, to thereby learn at least part of parameters of the main CNN.