A method implemented by a computing device, the method to perform batch normalization by using feature values of each batch including its corresponding input road images, wherein the batches include a first to an m-th batches, wherein: (a) instructing a convolutional layer of a CNN (i) to acquire a k-th batch, wherein k is an integer from 1 to m, and (ii) to generate feature maps for the k-th batch by applying convolution operations to the input road images included in the k-th batch respectively; and (b) instructing a batch normalization layer of the CNN to calculate one or more adjusted averages and one or more adjusted variations of the feature maps for the k-th batch by referring to (i) the feature maps for the k-th batch in case k is 1, and (ii) the feature maps for the k-th batch and previous feature maps, included in at least part of previous batches selected among the first to a (k-1)-th batches, which were previously generated in case k is an integer from 2 to m, and to generate adjusted feature maps by applying batch normalization operations to the feature maps for the k-th batch by referring to the adjusted averages and the adjusted variations, and generating CNN output values by applying output operations to the n-th re-adjusted feature maps, wherein the output operations are operations for a segmentation, a clustering, or an object detection; wherein, at the step of (b), by referring to variations of the feature maps for the k-th batch, the batch normalization layer determines the number of the batches, selected among the first to the (k-1)-th batches, to be used for generating the adjusted averages and the adjusted variations, and wherein, if variations of the feature maps for the k-th batch are smaller than a specific threshold value, the batch normalization layer calculates the adjusted averages and the adjusted variations by referring to (i) the feature maps for the k-th batch only or (ii) the feature maps for the k-th batch and specific previous feature maps for one or more specific previous batches, wherein the specific previous batches are selected among the first to the (k-1)-th batches such that a ratio of the number of the specific previous batches to (k-1) is less than a predefined number. (a) instructing a convolutional layer of a CNN (i) to acquire a k-th batch, wherein k is an integer from 1 to m, and (ii) to generate feature maps for the k-th batch by applying convolution operations to the input road images included in the k-th batch respectively; and (b) instructing a batch normalization layer of the CNN to calculate one or more adjusted averages and one or more adjusted variations of the feature maps for the k-th batch by referring to (i) the feature maps for the k-th batch in case k is 1, and (ii) the feature maps for the k-th batch and previous feature maps, included in at least part of previous batches selected among the first to a (k-1)-th batches, which were previously generated in case k is an integer from 2 to m, and to generate adjusted feature maps by applying batch normalization operations to the feature maps for the k-th batch by referring to the adjusted averages and the adjusted variations, and generating CNN output values by applying output operations to the n-th re-adjusted feature maps, wherein the output operations are operations for a segmentation, a clustering, or an object detection; wherein, at the step of (b), by referring to variations of the feature maps for the k-th batch, the batch normalization layer determines the number of the batches, selected among the first to the (k-1)-th batches, to be used for generating the adjusted averages and the adjusted variations, and wherein, if variations of the feature maps for the k-th batch are smaller than a specific threshold value, the batch normalization layer calculates the adjusted averages and the adjusted variations by referring to (i) the feature maps for the k-th batch only or (ii) the feature maps for the k-th batch and specific previous feature maps for one or more specific previous batches, wherein the specific previous batches are selected among the first to the (k-1)-th batches such that a ratio of the number of the specific previous batches to (k-1) is less than a predefined number. wherein, at the step of (b), by referring to variations of the feature maps for the k-th batch, the batch normalization layer determines the number of the batches, selected among the first to the (k-1)-th batches, to be used for generating the adjusted averages and the adjusted variations, and wherein, if variations of the feature maps for the k-th batch are smaller than a specific threshold value, the batch normalization layer calculates the adjusted averages and the adjusted variations by referring to (i) the feature maps for the k-th batch only or (ii) the feature maps for the k-th batch and specific previous feature maps for one or more specific previous batches, wherein the specific previous batches are selected among the first to the (k-1)-th batches such that a ratio of the number of the specific previous batches to (k-1) is less than a predefined number.