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: The invention relates to a method for detecting line structures (14) in image data and determining their progression. The method comprises the steps of capturing (30) image data by means of an image sensor, dividing (32) the entire image data into a plurality of cells (18) and assigning at least one predefined and oriented line (22) of fixed length to each cell (18), and dividing (34) the at least one line (22) into a predetermined number of line segments (38).

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: A method for training one or more neural networks for processing measurement data includes providing training examples for the measurement data including both training examples labeled with target classification scores and unlabeled training examples, and processing the training examples by the one or more neural networks into classification scores. The method further includes, with respect to the labeled training examples, using a specified cost function to evaluate to what extent (i) the classification scores correspond to the respective target classification scores, and (ii) intermediate products formed from similar training examples are similar to each other while intermediate products formed from dissimilar training examples are dissimilar to each other. The method further includes optimizing parameters characterizing a behavior of the one or more neural networks with the goal that an assessment by the cost function is expected to improve during further processing of the training examples.

Abstract: A method for determining a lane marking of a first lane for a vehicle. The method includes: providing measurement data from a monitoring of the surroundings of the vehicle; feeding the measurement data to at least one machine learning model; evaluating a course of the lane marking using the at least one machine learning model; evaluating a width of the lane marking using the at least one machine learning model. A method is also described for training at least one machine learning model for use in the above-mentioned method.

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 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.