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 and device for creating a digital map. The method includes: receiving first training data sets; generating second training data sets by means of a generative neural network by determining a second, perturbed image of the same surrounding area in each case for each first image, wherein each second training data set represents the second image of the corresponding first image; training a further neural network; receiving surrounding area data sets, which in each case represent a surrounding area image of a vehicle surrounding area, wherein these surrounding area data sets comprise a position description of the corresponding vehicle surrounding area; creating the digital map by merging the surrounding area images, depending on the position description, by means of a scan matching method, wherein the scan matching method includes at least the further neural network, and a step of providing the digital map.

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 recognizing horizontal road markings and determining the course thereof. The method includes the steps of capturing an image of a road, and dividing a central region of the image into a plurality of vertically superimposed cells and assigning to each cell predefined lines that are variously aligned around a horizontal direction. In a further step, at least one probability value for the presence of a road marking and displacement values of the line to the road marking are calculated for each line of each cell. The probability values and the displacement values are subsequently entered into a calculation function, and at least one line is output. The course of the horizontal road marking is determined from the at least one line and the displacement values.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: A method for training a convolutional neural network. For each of a multiplicity of training input images, the method includes processing of the training input image by the convolutional network; processing of a scaled version of the training input image by the convolutional network; determining a pair of convolutional layers of the convolutional network so that a convolutional layer of the pair generates a first feature map for the training input image which has the same size as a second feature map which is generated by the other convolutional layer of the pair for the scaled version of the training input image; and calculating a loss between the first feature map and the second feature map; and training the convolutional neural network to reduce an overall loss which includes the calculated losses.

Abstract: A computer-implemented method for training a machine learning system for transferring images of a source domain into a target domain. The method includes: ascertaining source patches based on source images of a source domain and target patches based on target images of a target domain, the source patches and the target patches each being assigned pixel-by-pixel pieces of meta-information; ascertaining tuples, each including one source patch and at least one target patch which characterizes a neighbor of the source patch nearest to k according to a similarity measure, k being a hyperparameter of the method and the similarity measure characterizing a similarity between a source patch and a target patch based on the pixel-by-pixel meta-information of the source patch and of the target patch; training the machine learning system based on the source patches of the tuples and on the target patches of the tuples.

Abstract: Computer-implemented method for training a machine learning system. The method includes: providing a source image from a source domain and a target image of a target domain; determining a first generated image based on the source image using a first generator, and determining a first reconstruction based on the first generated image using a second generator; determining a second generated image based on the target image using the second generator, and determining a second reconstruction based on the second generated image using the first generator; determining a first loss value, the first loss value characterizing a first difference between the source image and the first reconstruction, and determining a second loss value, the second loss value characterizing a second difference between the target image and the second reconstruction; and training the machine learning system based on the first loss value and/or the second loss value.

Abstract: A method for determining at least one anchor for an anchor-based lane line recognition and/or roadway marking recognition in a digital image representation on the basis of sensor data that are obtained from at least one surroundings sensor of a system. The method includes at least the following steps: a) receiving a digital image representation, b) setting at least one row or one column of possible anchors in at least one area of the digital image representation, the row or column of possible anchors being situated at a distance from at least the upper and lower or left and right edge of the area of the digital image representation.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: A device and method for processing data, in particular unnormalized, multidimensional data, of a neural network, in particular a deep neural network, especially for detecting objects in an input image. The data includes at least one first classification value for a multitude of positions in the input image in each case, a classification value quantifying a presence of a class. The method includes the following steps: evaluating the data as a function of a threshold value, a first classification value for a respective position in the input image that lies either below or above the threshold value being discarded, and a first classification value for a respective position in the input image that lies either above or below the threshold value not being discarded.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least two objects are determined. At least one relational attribute is determined for the at least two objects. The at least one relational attribute defines a relationship between the at least two objects. An object is recognized taking account of the at least one relational attribute.

Abstract: A method for recognizing an object from input data is disclosed. Raw detections are carried out in which at least one attribute in the form of a detection quality is determined for each raw detection. At least one further attribute for each raw detection is determined. A temporally or spatially resolved distance measure is determined for at least one attribute of the raw detections. Raw detections of a defined distance measure are combined to form a group of raw detections. The object is recognized from a group with at least one raw detection with the smallest distance measure of the at least one attribute in comparison with another raw detection, or from a group with at least one raw detection which were combined by combining at least two raw detections with the smallest distance measure of the at least one attribute to form said one raw detection.

Abstract: An operating device for a shift by wire assembly in an automobile includes a selection apparatus configured to select an operation mode (R, N, D) of the automobile, wherein the selectable operation modes comprise a reverse operation mode (R), a neutral operation mode (N) and a drive operation mode (D), wherein the selection apparatus is further configured to select an autonomous drive operation mode (AD).

Abstract: Method for obtaining information from short-fibre-reinforced plastic components sequentially produced by an X-ray computed tomography. A learning phase includes: generating CT data sets for a random sample of plastic components from a production process; extracting at least one defect-free region of the plastic components; determining characteristic feature(s) in the extracted regions, relevance of individual features, and regions which are characteristic of the plastic component type and production process thereof, over a predetermined period of the plastic components productions, which exhibit considerable characteristic differences between good parts and reject parts; and defining the feature(s) with its characteristic as trained classifier.

Abstract: The present invention refers to a loudspeaker, in especially to a bass reflex tube for a loudspeaker. Commonly, said loudspeakers include at least one woofer driver, by which an electric audio signal is transduced via a voice coil and a diaphragm into soundwaves of medium to low frequency. A bass reflex tube for a loudspeaker should be provided, which enables said loudspeaker to produce an acoustic signal according to an electrical signal, wherein the acoustic signal shows optimized an improved quality characteristics, in especially with regards to quality and accuracy of the conversion of electrical signal into the acoustic signal. The problem mentioned above is solved by a bass reflex tube for a loudspeaker, wherein said bass reflex tube is at least partially made of a ceramic material. Further, the bass reflex tube comprises a tubular portion, which is extending in an axial direction of a longitudinal axis of the bass reflex tube.

Abstract: Method for obtaining information from short-fibre-reinforced plastic components sequentially produced by an X-ray computed tomography. A learning phase includes: generating CT data sets for a random sample of plastic components from a production process; extracting at least one defect-free region of the plastic components; determining characteristic feature(s) in the extracted regions, relevance of individual features, and regions which are characteristic of the plastic component type and production process thereof, over a predetermined period of the plastic components productions, which exhibit considerable characteristic differences between good parts and reject parts; and defining the feature(s) with its characteristic as trained classifier.

Abstract: Method for obtaining at least one significant feature in a series of components of the same type on the basis of data sets by non-destructive testing. The method includes examining a classified random sample of components which have a known production sequence, by a non-destructive testing. A three-dimensional data set for each component is obtained, and components of the sample are divided by good and rejected parts. Defect-free component regions from all of the components of the random sample are extracted. At least one feature which is characteristic of the type of component and production process which, over a predetermined time of component production, exhibits considerable characteristic differences between the good and rejected parts is determined. The determination can be accomplished using neural networks, machine learning approaches, or statistics from the field of data analytics. The at least one feature and its characteristic is defined as a trained classifier.

Abstract: The present invention refers to a loudspeaker, in especially to a bass reflex tube for a loudspeaker. Commonly, said loudspeakers include at least one woofer driver, by which an electric audio signal is transduced via a voice coil and a diaphragm into soundwaves of medium to low frequency. A bass reflex tube for a loudspeaker should be provided, which enables said loudspeaker to produce an acoustic signal according to an electrical signal, wherein the acoustic signal shows optimized an improved quality characteristics, in especially with regards to quality and accuracy of the conversion of electrical signal into the acoustic signal. The problem mentioned above is solved by a bass reflex tube for a loudspeaker, wherein said bass reflex tube is at least partially made of a ceramic material. Further, the bass reflex tube comprises a tubular portion, which is extending in an axial direction of a longitudinal axis of the bass reflex tube.