A learning method for calculating collision probability, to be used for determining whether it is appropriate or not to switch driving modes of a vehicle capable of an autonomous driving, by analyzing a recent driving route of a driver with regard to a circumstance of its corresponding time range, comprising steps of: (a) a learning device, on condition that (i) at least one status vector, corresponding to at least one piece of circumstance information for verification including at least part of (i-1) at least one piece of subject motion information on at least one subject vehicle and (i-2) one or more pieces of surrounding motion information on at least part of one or more surrounding objects located closer than a threshold from the subject vehicle, in a subject time range for verification from a first timing to a T-th timing, and (ii) at least one trajectory vector, corresponding to at least one piece of route information for verification on at least one driving route driven by the subject vehicle in the subject time range for verification, are acquired, performing processes of (i) instructing a status network to apply at least one first neural network operation to the status vector, to thereby generate at least one status feature map and (ii) instructing a trajectory network to apply at least one second neural network operation to the trajectory vector, to thereby generate at least one trajectory feature map; (b) the learning device, if at least one concatenated feature map corresponding to the status feature map and the trajectory feature map is acquired, instructing a safety network to apply at least one third neural network operation to the concatenated feature map, to thereby calculate at least one predicted collision probability representing a predicted probability of an occurrence of at least one accident caused by the driving route indicated by the route information for verification with regard to a circumstance indicated by the circumstance information for verification, wherein the predicted collision probability is used for determining whether it is appropriate or not to switch driving modes of the subject vehicle from a manual driving mode to an autonomous driving mode; and (c) the learning device instructing a loss layer to generate at least one loss by referring to the predicted collision probability and at least one Ground-Truth, GT, collision probability, which have been acquired beforehand, and to perform backpropagation by using the loss, to thereby learn at least part of parameters of the safety network, the trajectory network and the status network, wherein, at the step of (b), the learning device instructs (i) at least one concatenating layer to generate the concatenated feature map by concatenating the status feature map and the trajectory feature map, (ii) at least one third convolutional layer of the safety network to generate at least one -st feature map by applying at least one third convolutional operation to the concatenated feature map, (iii) at least one third pooling layer of the safety network to generate at least one -nd feature map by applying at least one third pooling operation to the -st feature map, and (iv) at least one third Fully-Connected, FC, layer to generate the predicted collision probability by applying at least one third FC operation to the -nd feature map. (a) a learning device, on condition that (i) at least one status vector, corresponding to at least one piece of circumstance information for verification including at least part of (i-1) at least one piece of subject motion information on at least one subject vehicle and (i-2) one or more pieces of surrounding motion information on at least part of one or more surrounding objects located closer than a threshold from the subject vehicle, in a subject time range for verification from a first timing to a T-th timing, and (ii) at least one trajectory vector, corresponding to at least one piece of route information for verification on at least one driving route driven by the subject vehicle in the subject time range for verification, are acquired, performing processes of (i) instructing a status network to apply at least one first neural network operation to the status vector, to thereby generate at least one status feature map and (ii) instructing a trajectory network to apply at least one second neural network operation to the trajectory vector, to thereby generate at least one trajectory feature map; (b) the learning device, if at least one concatenated feature map corresponding to the status feature map and the trajectory feature map is acquired, instructing a safety network to apply at least one third neural network operation to the concatenated feature map, to thereby calculate at least one predicted collision probability representing a predicted probability of an occurrence of at least one accident caused by the driving route indicated by the route information for verification with regard to a circumstance indicated by the circumstance information for verification, wherein the predicted collision probability is used for determining whether it is appropriate or not to switch driving modes of the subject vehicle from a manual driving mode to an autonomous driving mode; and (c) the learning device instructing a loss layer to generate at least one loss by referring to the predicted collision probability and at least one Ground-Truth, GT, collision probability, which have been acquired beforehand, and to perform backpropagation by using the loss, to thereby learn at least part of parameters of the safety network, the trajectory network and the status network,