The invention relates to a danger ranaking training method, a danger ranking method, an auxiliary or automatic vehicle driving system. The danger ranking training method comprises the steps of training a first deep neural network for generic object recognition within generic images; training a second deep neural network for specific object recognition within images related to the specific application; training a third deep neural network for specific scene flow prediction within image sequences related to the specific application; training a fourth deep neural network for potential danger areas localization within images or image sequences related to the specific application using at least one human trained danger tagging method; training a fifth deep neural network for non-visible specific object anticipation and/or visible specific object prediction within image or image sequences related to the specific application; and determining at least one danger pixel within an image or an image sequence related to the specific application using an end-to-end deep neural network as a sequence of transfer learning of the five deep neural networks followed by one or several end-to-end top layers.
Danger ranking training method, danger ranking method, auxiliary or automatic vehicle driving system