Abstract: A method for generating training data for a recognition model for recognizing objects in sensor data of a sensor. Objects and object attributes are recognized in auxiliary sensor data of an auxiliary sensor mapping at least one overlapping area using a trained auxiliary recognition model, and the object attributes of the objects recognized in the overlapping area being transferred to the sensor data mapping at least the overlapping area in order to generate training data.

Abstract: An iterative method is for determining training data for a primary model to solve a primary recognition task. The iterative method includes a) providing at least one labeled training sample, b) training the primary model with the at least one labeled training sample, c) providing at least one labeled test sample, and d) evaluating a recognition performance of the primary model using the labeled test sample on the primary recognition task. The iterative method further includes, depending on a result of the evaluating the recognition performance, either (i) re-performing parts a), b), c), and d) of the iterative method, or (ii) ending the iterative method.

Abstract: A method for evaluating sensor data. The sensor data are ascertained by scanning a surrounding area, using at least one sensor. On the basis of the sensor data, object detection is carried out for determining objects from the sensor data. Object filtering is carried out. Surface characteristics of at least one object are identified, and/or the surface characteristics of at least one object are ascertained with the aid of access to a database. A control unit is also described.

Abstract: A method and a labeling system for generating a label object for the symbolic description of an object of an environment of a mobile device, e.g., a robot or a vehicle. The label object includes at least one attribute of an object at a first point in time, from observations of this object. The method includes selecting, from the observations, a first observation recorded at a first point in time, a second observation recorded at a second point in time, the second point in time being a point in time before the first point in time, as well as a third observation recorded at a third point in time, the third point in time being a point in time after the first point in time; and ascertaining, by using the selected observations, the at least one attribute of the object.

Abstract: The present disclosure relates to a method for generating training data for a recognition model for recognizing objects in sensor data of a vehicle. First sensor data and second sensor data are input into a learning algorithm. The first sensor data comprise measurements of a first surroundings sensor. The second sensor data comprise a measurements of a second surroundings sensor. A training data generation model is generated, using learning algorithm, that generates measurements of the second surroundings sensor assigned to measurements of the first surroundings sensor. First simulation data are input into the training data generation model. The first simulation data comprise simulated measurements of the first surroundings sensor. Second simulation data are generated as the training data based on the first simulation data using the training data generation model. The second simulation data comprise simulated measurements of the second surroundings sensor.

Abstract: A method and a device for the functional testing and evaluation of a control system. The method for functional testing of sensors, actuators, and/or a control logic system of a control system controlled in at least a partly automated manner has the following: sending at least one stimulus to a sensor, an actuator, and/or to the control logic system of the control system; reading out at least one reaction of the control system from the sensor, from the actuator, from the control logic system, and/or from a connection between these components; and using a metric module to compare the reaction with a stored expected reaction, the metric module determining a metric according to which deviations of the read-out reaction from the expected reaction are to be evaluated with respect to the intended function of the control system.

Abstract: A method for evaluating sensor data. The sensor data are ascertained by scanning a surrounding area, using at least one sensor. On the basis of the sensor data, object detection is carried out for determining objects from the sensor data. Object filtering is carried out. Surface characteristics of at least one object are identified, and/or the surface characteristics of at least one object are ascertained with the aid of access to a database. A control unit is also described.

Abstract: A method for generating labels for a data set. The method includes: providing an unlabeled data set comprising a number of unlabeled data; generating initial labels for the data of the unlabeled data set; providing the initial labels as nth labels where n=1; performing an iterative process, where an nth iteration of the iterative process comprises the following steps for every n=1, 2, 3, . . . N: training a model as an nth trained model using a labeled data set, the labeled data set being given by a combination of the data of the unlabeled data set with the nth labels; predicting nth predicted labels for the unlabeled data of the unlabeled data set by using the nth trained model; determining (n+1)th labels from a set of labels comprising at least the nth predicted labels.

Abstract: A method and a device for generating labeled data, for example training data, in particular for a neural network.

Abstract: A method for training a machine learning model for determining a quality grade of data sets from each of a plurality of sensors. The sensors are configured to generate surroundings representations. The method includes: providing data sets of each of the sensors from corresponding surroundings representations; providing attribute data of ground truth objects of the surroundings representations; determining a quality grade of the respective data set of each of the sensors using a metric, the metric comparing at least one variable, which is determined using the respective data set, with at least one attribute datum of at least one associated ground truth object of the surroundings representation; and training the machine learning model using the data sets of each of the sensors and the respectively assigned determined quality grades.

Abstract: A method for generating training data for a recognition model for recognizing objects in sensor data of a sensor. Objects and object attributes are recognized in auxiliary sensor data of an auxiliary sensor mapping at least one overlapping area using a trained auxiliary recognition model, and the object attributes of the objects recognized in the overlapping area being transferred to the sensor data mapping at least the overlapping area in order to generate training data.

Abstract: A method for generating a reference representation of the surroundings of an at least semi-automated mobile platform is provided. The method includes ascertaining at least one third-party platform for receiving reference data for the reference representation of the surroundings of the mobile platform; establishing a communication channel of the mobile platform to the at least one third-party platform; receiving the reference data of the at least one third-party platform for the reference representation of the surroundings; ascertaining the received reference data that are relevant to the generation of the reference representation; storing the relevant reference data of the at least one third-party platform for the generation of the reference representation of the surroundings.

Abstract: A method for ascertaining a ground profile in a region in front of a vehicle in a direction of travel, using at least one sensor; at least one vertical motion of at least one vehicle traveling ahead being measured by the vehicle, an independent motion of the vehicle being ascertained or estimated, the measured data of the at least one vertical motion of the at least one vehicle traveling ahead being compared to the ascertained or estimated data of the independent motion of the vehicle, and a ground profile being derived from the comparison of the measured data of the at least one vehicle traveling ahead with the ascertained or estimated data of the independent motion of the vehicle. In addition, a system is described.

Abstract: A method and a labeling system for generating a label object for the symbolic description of an object of an environment of a mobile device, e.g., a robot or a vehicle. The label object includes at least one attribute of an object at a first point in time, from observations of this object. The method includes selecting, from the observations, a first observation recorded at a first point in time, a second observation recorded at a second point in time, the second point in time being a point in time before the first point in time, as well as a third observation recorded at a third point in time, the third point in time being a point in time after the first point in time; and ascertaining, by using the selected observations, the at least one attribute of the object.

Abstract: A method and a device for the functional testing and evaluation of a control system. The method for functional testing of sensors, actuators, and/or a control logic system of a control system controlled in at least a partly automated manner has the following: sending at least one stimulus to a sensor, an actuator, and/or to the control logic system of the control system; reading out at least one reaction of the control system from the sensor, from the actuator, from the control logic system, and/or from a connection between these components; and using a metric module to compare the reaction with a stored expected reaction, the metric module determining a metric according to which deviations of the read-out reaction from the expected reaction are to be evaluated with respect to the intended function of the control system.

Abstract: A method for ascertaining a ground profile in a region in front of a vehicle in a direction of travel, using at least one sensor; at least one vertical motion of at least one vehicle traveling ahead being measured by the vehicle, an independent motion of the vehicle being ascertained or estimated, the measured data of the at least one vertical motion of the at least one vehicle traveling ahead being compared to the ascertained or estimated data of the independent motion of the vehicle, and a ground profile being derived from the comparison of the measured data of the at least one vehicle traveling ahead with the ascertained or estimated data of the independent motion of the vehicle. In addition, a system is described.