A risk prediction method executed by a computer of a risk predictor using a convolutional neural network, the method including making the convolutional neural network acquire an input image taken by an in-vehicle camera installed on a vehicle, making the convolutional neural network estimate a risk area and a feature of the risk area, the risk area being in the acquired input image, the risk area having a possibility that a moving object may appear from the risk area into a travelling path of the vehicle and the moving object may collide with the vehicle in a case where the vehicle simply continues running, and making the convolutional neural network output the estimated risk area and the estimated feature of the risk area as a risk predicted for the input image.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A risk prediction method executed by a computer of a risk predictor using a convolutional neural network, the method comprising: making the convolutional neural network acquire an input image taken by an in-vehicle camera installed on a vehicle; making the convolutional neural network estimate a risk area and a feature of the risk area, the risk area being in the acquired input image, the risk area having a possibility that a moving object may appear from the risk area into a travelling path of the vehicle and the moving object may collide with the vehicle in a case where the vehicle simply continues running, and then making the convolutional neural network output the estimated risk area and the estimated feature of the risk area as a risk predicted for the input image. 2. The risk prediction method according to claim 1, wherein in the outputting, the convolutional neural network is made to estimate a degree of risk of the risk area as the feature of the risk area and output, as the risk, a likelihood map indicating the estimated risk area and the degree of risk of the risk area. 3. The risk prediction method according to claim 1, wherein in the outputting, the convolutional neural network is made to estimate a type of the moving object related to the risk area as the feature of the risk area and output, as the risk, the estimated risk area and the type. 4. The risk prediction method according to claim 1, further comprising 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 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. 5. The risk prediction method according to claim 4, wherein the learning includes making a first neural network, which is a convolutional neural network including a fully connected layer, learn a first weight of the first neural network by using a risk area image and a safe area image which are each a partial area of the learning image, the risk area image being the area added with the annotation indicating the risk area, the safe area image being an area to which the annotation is not added, such that the first weight of the first neural network for determining whether the partial area is a safe area or risk area is learned, assigning the learned first weight as an initial value of a weight of a second neural network having a configuration obtained by changing the fully connected layer of the first neural network to a convolutional layer, and making the second neural network learn a second weight of the second neural network for estimating the risk area in the learning image and the feature of the risk area by using the learning image including the risk area and the annotated image generated by adding the annotation indicating the risk area to the learning image, thereby learning the weight of the convolutional neural network having the same configuration as that of the second neural network. 6. The risk prediction method according to claim 4, further comprising: acquiring a plurality of temporally successive images taken by an in-vehicle camera installed on a vehicle; and determining a risk area included in at least part of the plurality of acquired images and having a possibility that a moving object may appear from the risk area into a travelling path of the vehicle and the moving object may collide with the vehicle in a case where the vehicle simply continues running; and adding an annotation indicating the determined risk area to the at least part of the plurality of images, wherein in the learning, the at least part of the plurality images having the added annotation and images corresponding to the at least part of the plurality of images are acquired as the learning image and the annotated image, and the weight of the convolutional neural network is learned using the acquired learning image and the annotated image. 7. The risk prediction method according to claim 4, wherein the risk area 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. 8. The risk prediction method according to claim 4, wherein the risk area is an area between two or more moving objects including a person at least one of which has a possibility of moving toward the other one of the moving objects and crossing the travelling path of the vehicle. 9. The risk prediction method according to claim 4, wherein the annotation indicates the risk area and a category of the moving object related to the risk area. 10. The risk prediction method according to claim 4, wherein the annotation indicates the risk area and control information including information in terms of a brake strength or a steering wheel angle of the vehicle as of when the learning image is taken. 11. The risk prediction method according to claim 4, wherein the annotation is segment information on the risk area. 12. The risk prediction method according to claim 1, wherein at least one of the acquiring and the outputting is performed by a processor of the computer.