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: 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 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 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.