A self-driving car would need to detect multiple things simultaneously, such as pedestrians, stop signs, other cars, and traffic lights. So in an input image, we would have four labels to check for each of the four objects. This gives us a y(i) vector of dimension 4 x 1, and consequently our training data labels would be of dimension 4 x m, where m is the number of y(i) vectors taken. We can now use a neural network to predict values of y by inputting an image and getting an output four dimensional matrix. This method also works if a few of the labels are missing in some of the input images.