A computer-implemented method for estimating a risk of collision of an automated driving vehicle and a moving object based on an input image and for controlling the automated driving vehicle, using a convolutional neural network, the method comprising: making the convolutional neural network acquire the input image taken by an in-vehicle camera installed on the automated driving vehicle; making the convolutional neural network estimate a risk area within the input image and a degree of risk of the risk area as a feature of the risk area, with the risk area being an area from which the moving object may appear into a travelling path of the automated driving vehicle and the moving object may collide with the automated driving vehicle in a case where the automated driving vehicle simply continues running; making the convolutional neural network output the estimated risk area and the estimated feature of the risk area as the predicted risk for the collision, wherein the predicted risk for the collision is a likelihood map indicating the estimated risk area and the degree of risk of the risk area; controlling the driving of the automated driving vehicle so as to avoid a physical area corresponding to the estimated risk area; and making the convolutional neural network, before the acquiring of the input image, learn a weight of the convolutional neural network by using a learning image including the risk area, which is an area including a part of an area of a hiding object existing in the learning image in a state in which the moving object is hidden behind before the moving object appears from the hiding object into the travelling path, and an annotated image generated by adding an annotation indicating the risk area to the learning image such that the weight for estimating the risk area in the learning image and the feature of the risk area is learned. making the convolutional neural network acquire the input image taken by an in-vehicle camera installed on the automated driving vehicle; making the convolutional neural network estimate a risk area within the input image and a degree of risk of the risk area as a feature of the risk area, with the risk area being an area from which the moving object may appear into a travelling path of the automated driving vehicle and the moving object may collide with the automated driving vehicle in a case where the automated driving vehicle simply continues running; making the convolutional neural network output the estimated risk area and the estimated feature of the risk area as the predicted risk for the collision, wherein the predicted risk for the collision is a likelihood map indicating the estimated risk area and the degree of risk of the risk area; controlling the driving of the automated driving vehicle so as to avoid a physical area corresponding to the estimated risk area; and making the convolutional neural network, before the acquiring of the input image, learn a weight of the convolutional neural network by using a learning image including the risk area, which is an area including a part of an area of a hiding object existing in the learning image in a state in which the moving object is hidden behind before the moving object appears from the hiding object into the travelling path, and an annotated image generated by adding an annotation indicating the risk area to the learning image such that the weight for estimating the risk area in the learning image and the feature of the risk area is learned. <EOS>