Abstract: A method and first device for creating a digital map. The digital map represents at least one region along a traffic route. A method and second device for operating an automated vehicle along a traffic route is also described.

Abstract: A method for positioning a map representation of an environment of a vehicle in a semantic road map. The method includes creating a map representation of an environment of the vehicle based on environmental sensor data; ascertaining characteristic elements in the map representation and in a semantic road map representing the environment; comparing the characteristic elements of the map representation to the characteristic elements of the semantic road map; ascertaining a portion of the semantic road map for which at least one characteristic element of the portion of the semantic road map matches at least one characteristic element of the map representation and for which the map representation and the semantic road map describe an identical region of the environment of the vehicle; and identifying a position of the portion within the semantic road map as the position of the map representation in the semantic road map.

Abstract: A method for generating training datasets for training an evaluation algorithm using which an alignment of two map datasets can be evaluated to determine navigation information for a mobile device that is moving or is to move in an environment. The method includes, for each of a plurality of training datasets: providing two input feature datasets, wherein underlying map datasets each contain environmental information that has been acquired using a sensor of the mobile device; providing a transformation dataset generated during an alignment of the two input feature datasets; providing a reference transformation dataset as ground truth; determining a correlation dataset based on the two input feature datasets and/or the transformation dataset; determining a quality measure depending on an accuracy of a match between the transformation dataset and the reference transformation dataset; and providing the training dataset which includes the correlation dataset and the quality measure.

Abstract: A method for training a machine learning model for processing multimodal data. The machine learning model includes a first and a second encoder, a transformer, a first pose regressor head, and a second pose regressor head. The method includes: merging the features of the first and of the second sensor data into a common feature embedding space by the transformer; decoding the merged features from the common feature embedding space for outputting a pose estimate for the first sensor by the first pose regressor head; decoding the merged features from the common feature embedding space for outputting a pose estimate for the second sensor by the second pose regressor head; minimizing a loss function for optimizing the pose estimation for the first and the second sensor; and providing the trained machine learning model for processing multimodal data.

Abstract: A method for training a machine learning model which is configured to identify easily recognizable key points in an input image. The method includes: providing a set of training images; transforming each training image into a variation which contains contents of the training image at other positions; adding synthetically generated image contents to each training image and to its variation, which show the same semantic contents from different perspectives; ascertaining key points for the training image on the one hand and for the variation on the other, using the machine learning model; evaluating using a given cost function the extent to which corresponding key points of the training image and its variation relate to corresponding image contents; and optimizing parameters characterizing the behavior of the machine learning model, with the aim of improving the evaluation by the cost function during further processing of training images and variations.

Abstract: A method for matching a digital road map to a digital reference road map, wherein the two road maps in each case include semantic information about objects represented on the two road maps. The method includes: associating objects on the road map with objects on the reference road map on the basis of the respective semantic information such that only objects with the same semantic information are associated with one another; matching the road map to the reference road map on the basis of the association, wherein the matching comprises creating a delta pose between the road map and the reference road map. A device, a computer program, and a machine-readable storage medium are also described.

Abstract: A method for creating map data for the operation of an automated and/or assisting system of a vehicle or robot. A map for a traffic route portion of a traffic route is compared with a reference map for the traffic route portion on an automated basis. The map is used exclusively for creating the map data on an automated basis when the map does not deviate from the reference map by a predefined extent with regard to at least one predefined quality measure.

Abstract: A method and an apparatus for training a machine learning model comprising a first and a second deep neural network.

Abstract: An adapter is provided for securing a filling tube of a fuel container to a body of a motor vehicle. The adapter has a contact region for placement on the body and a first securing point for securing a first holder for the filling tube. This allows for it to be easily adapted to different vehicle body shapes.

Abstract: A method for generating training datasets for training an evaluation algorithm using which an alignment of two map datasets can be evaluated to determine navigation information for a mobile device that is moving or is to move in an environment. The method includes, for each of a plurality of training datasets: providing two input feature datasets, wherein underlying map datasets each contain environmental information that has been acquired using a sensor of the mobile device; providing a transformation dataset generated during an alignment of the two input feature datasets; providing a reference transformation dataset as ground truth; determining a correlation dataset based on the two input feature datasets and/or the transformation dataset; determining a quality measure depending on an accuracy of a match between the transformation dataset and the reference transformation dataset; and providing the training dataset which includes the correlation dataset and the quality measure.

Abstract: A method for matching a digital road map to a digital reference road map, wherein the two road maps in each case include semantic information about objects represented on the two road maps. The method includes: associating objects on the road map with objects on the reference road map on the basis of the respective semantic information such that only objects with the same semantic information are associated with one another; matching the road map to the reference road map on the basis of the association, wherein the matching comprises creating a delta pose between the road map and the reference road map. A device, a computer program, and a machine-readable storage medium are also described.

Abstract: A method for aligning two map datasets. The method includes: providing two map datasets, each containing environmental information, wherein the environmental information in the two map datasets has been detected by a sensor of a mobile device, and at least one of the two map datasets is a sparse map dataset; providing the two map datasets as input feature data or determining input feature data based on the two map datasets; carrying out an alignment of the two map datasets using a machine learning algorithm based on sparse convolution, wherein output data including information about a transformative relation between the two map datasets are generated from the input feature data, via intermediate feature data in one or more intermediate layers.

Abstract: A method for aligning a first map section of a digital road map with a second map section of the digital road map that at least partially overlaps the first map section. The method includes: determining that a first relative rotation between the two map sections cannot be unambiguously ascertained; ascertaining a second relative rotation between a third map section of the digital road map and a fourth map section of the digital road map that at least partially overlaps the third map section, wherein the third map section and the fourth map section are adjacent to the first map section and to the second map section; aligning the first map section with the second map section; wherein the alignment includes ascertaining a relative rotation between the first map section and the second map section based on the ascertained second relative rotation.

Abstract: A method for training a neural network, which, based on coordinates of a location in a scene and information about a perspective from which the location is viewed, predicts color information long with values of at least one further physical measured variable that relate to this location. The method includes: capturing both camera images of the scene and values of the further physical measured variable as training examples from a plurality of perspectives; supplying coordinates of locations at which color information and/or values of the at least one further physical measured variable can be captured from every perspective, together with information characterizing the perspective, to the neural network; evaluating using a predetermined cost function.

Abstract: A method for positioning a map representation of an environment of a vehicle in a semantic road map. The method includes creating a map representation of an environment of the vehicle based on environmental sensor data; ascertaining characteristic elements in the map representation and in a semantic road map representing the environment; comparing the characteristic elements of the map representation to the characteristic elements of the semantic road map; ascertaining a portion of the semantic road map for which at least one characteristic element of the portion of the semantic road map matches at least one characteristic element of the map representation and for which the map representation and the semantic road map describe an identical region of the environment of the vehicle; and identifying a position of the portion within the semantic road map as the position of the map representation in the semantic road map.

Abstract: A method and a device for determining and providing environmental feature information. A method and a device for creating a map are also described.

Abstract: A method and first device for creating a digital map. The digital map represents at least one region along a traffic route. A method and second device for operating an automated vehicle along a traffic route is also described.

Abstract: An adapter is provided for securing a filling tube of a fuel container to a body of a motor vehicle. The adapter has a contact region for placement on the body and a first securing point for securing a first holder for the filling tube. This allows for it to be easily adapted to different vehicle body shapes.

Abstract: A method for measuring the influence of a transparent pane. In the method, a displacement field induced by the pane is determined. In a first step, a first image of a textured surface without the transparent pane is acquired; in a second step, a second image of the textured surface with the transparent pane is acquired; and in a third step, the displacement field is determined by analyzing the two images using an optical flow method.

Abstract: A pressure compensation valve for a dialysis machine is disclosed. The pressure compensation valve has a valve housing, in which a flow path with fresh dialysate is separated from a flow path with exhausted dialysate by a diaphragm. The diaphragm and the valve housing are configured such that pressure compensation is possible. In addition, the diaphragm is configured such that no vibrations or a reduced amount of vibrations and/or no noise emission or a reduced amount of noise emission will occur during operation. Furthermore, a dialysis machine comprising such a pressure compensation valve is disclosed. The dialysis machine comprises a balancing unit, and the pressure compensation valve is arranged on the outlet side of the balancing unit.