A learning method of a Convolutional Neural Network, CNN, for monitoring one or more blind spots of a monitoring vehicle, comprising steps of: (a) a learning device, if a training image corresponding to at least one video image taken from the monitoring vehicle is inputted, instructing a detector on the monitoring vehicle to output class information and location information on a monitored vehicle included in the training image, wherein the class information and the location information are results of detecting the monitored vehicle in the training image by the detector, wherein the class information is a class of an object related to the monitored vehicle and wherein the location image is a location corresponding to an area where the monitored vehicle is located in the training image including one or more coordinates of a bounding box in the training image; (b) the learning device instructing a cue information extracting layer (121; 321) to perform one or more operations by using the class information and the location information on the monitored vehicle, to thereby output one or more pieces of cue information on the monitored vehicle, wherein said pieces of cue information on the monitored vehicle include at least part of (i) the class information on the monitored vehicle, (ii) the location information on the monitored vehicle including additional coordinates of the bounding box corresponding to said one or more coordinates of the bounding box, (iii) size information on the monitored vehicle corresponding to a Region of Interest, ROI, size, (iv) aspect ratio information on the monitored vehicle, and (v) distance information between a center of the monitored vehicle and one of outer sides of the blind spots, and the learning device instructing a fully connected, FC, layer for monitoring the blind spots (122; 322) to perform one or more neural network operations by using said pieces of cue information or their processed values on the monitored vehicle, to thereby output a result of determining whether the monitored vehicle is located on one of the blind spots of the monitoring vehicle; and (c) the learning device instructing a first loss layer (123; 323) to generate one or more loss values for the blind spots by referring to the result and its corresponding first Ground Truth, GT, to thereby learn one or more parameters of the FC layer for monitoring the blind spots (122; 322)by backpropagating the loss values for the blind spots, and instructing a second loss layer to generate one or more loss values for vehicle detection by referring to the class information and the location information on the monitored vehicle and their corresponding second GT, to thereby learn one or more parameters of the detector by backpropagating the loss values for the vehicle detection. (a) a learning device, if a training image corresponding to at least one video image taken from the monitoring vehicle is inputted, instructing a detector on the monitoring vehicle to output class information and location information on a monitored vehicle included in the training image, wherein the class information and the location information are results of detecting the monitored vehicle in the training image by the detector, wherein the class information is a class of an object related to the monitored vehicle and wherein the location image is a location corresponding to an area where the monitored vehicle is located in the training image including one or more coordinates of a bounding box in the training image; (b) the learning device instructing a cue information extracting layer (121; 321) to perform one or more operations by using the class information and the location information on the monitored vehicle, to thereby output one or more pieces of cue information on the monitored vehicle, wherein said pieces of cue information on the monitored vehicle include at least part of (i) the class information on the monitored vehicle, (ii) the location information on the monitored vehicle including additional coordinates of the bounding box corresponding to said one or more coordinates of the bounding box, (iii) size information on the monitored vehicle corresponding to a Region of Interest, ROI, size, (iv) aspect ratio information on the monitored vehicle, and (v) distance information between a center of the monitored vehicle and one of outer sides of the blind spots, and the learning device instructing a fully connected, FC, layer for monitoring the blind spots (122; 322) to perform one or more neural network operations by using said pieces of cue information or their processed values on the monitored vehicle, to thereby output a result of determining whether the monitored vehicle is located on one of the blind spots of the monitoring vehicle; and (c) the learning device instructing a first loss layer (123; 323) to generate one or more loss values for the blind spots by referring to the result and its corresponding first Ground Truth, GT, to thereby learn one or more parameters of the FC layer for monitoring the blind spots (122; 322)by backpropagating the loss values for the blind spots, and instructing a second loss layer to generate one or more loss values for vehicle detection by referring to the class information and the location information on the monitored vehicle and their corresponding second GT, to thereby learn one or more parameters of the detector by backpropagating the loss values for the vehicle detection.