Abstract: Computer-implemented method for predicting one or more turn points related to a road a vehicle is travelling on, the one or more turn points indicating locations where the vehicle can change direction, the method comprising: obtaining training images of roads and their environment; receiving labels associated with the roads in the training images, each label comprising a training turn marker; training an artificial neural network on a training dataset to predict one or more turn points, wherein the training dataset comprises the received labels and the obtained training images; recording at least one road image of a road and its environment; and processing the road image by the artificial neural network to predict one or more turn points on the road image.

Abstract: A multimodal neural network model for combined depth estimation and semantic segmentation of images and a method of training the multimodal neural network model. The multimodal neural network comprising a single encoder, a depth decoder to estimate the depth of the image and a semantic segmentation decoder to determine semantic labels from the image. The method for training the multimodal neural network model comprising receiving a plurality of images at a single encoder, after encoding the images providing them to a depth estimation decoder and a semantic segmentation decoder to estimate the depth of the images and semantic labels to the images. The method further comprising comparing the estimated depth with the actual depth of the images and comparing the calculated semantic labels with the actual labels of the images to determine a depth loss and a semantic segmentation loss, respectively.

Abstract: Provided are a computer-implemented method and apparatus for predicting virtual road sign locations of virtual road signs that may be superimposed onto environmental data of a vehicle. The method includes collecting, as a first training data subset, one or more aerial and/or satellite images of a pre-determined region; obtaining, as a second training data subset, geocentric positions of key point markers in the pre-determined region; supplying the first and second training data subsets to a deep neural network as training dataset; training the deep neural network on the training dataset to predict key point marker locations in a region of interest, the key point marker locations corresponding to virtual road sign locations; defining a region of interest as input dataset; and processing the input dataset by the trained deep neural network to predict key point marker locations within the defined region of interest.

Abstract: Computer-implemented method for determining coordinates of navigation key points indicative of road sign locations and/or turn points, the method comprising: collecting, as a first training data subset, one or more first images of a first camera comprised in a mobile device; obtaining, as a second training data subset, image-related coordinates of navigation key points related to the images of the first training data subset; supplying the first training data subset and the second training data subset to an artificial neural network as a training dataset; training the artificial neural network on the training dataset to predict image-related coordinates of navigation key points indicative of road sign locations and/or turn points; capturing a second image of a second camera as an input dataset, processing the input dataset by the artificial neural network to predict image-related coordinates of navigation key points indicative of road sign locations and/or turn points.