Abstract: A method for automated data generation by neural network ensembles is disclosed. The method includes obtaining a cluster of trained ensembles of machine learning (ML) algorithms. The cluster includes two or more ML algorithm ensembles, wherein each ML ensemble includes a plurality of ML algorithms that are trained based on a first set of training data. The method further includes obtaining sensor data representative of a scenario, in a surrounding environment of a vehicle, wherein the sensor data includes at least two sensor data sets. The method further includes providing each obtained sensor data set as input to a corresponding ML algorithm ensemble. The method further includes selecting the ensemble-prediction output of one ML algorithm ensemble associated with an absent determined discrepancy for generating an annotation for one or more data samples of the sensor data set of at least one ML algorithm ensemble associated with a determined discrepancy.

Abstract: The present disclosure relates to methods, systems, a vehicle and a computer-readable storage medium and a computer program product. The method includes obtaining a trained ensemble of machine learning, ML, algorithms, including a plurality of ML algorithms that are trained at least partly based on a first set of training data. The method further includes forming an input data set for the trained ensemble of ML algorithms. The method further includes providing the formed input data set to the trained ensemble of ML algorithms for generating, for each of the one or more input data samples, an ensemble prediction output. In response to a determined discrepancy in the ensemble prediction output for a particular input data sample, the method further includes forming a second training data set based on that particular input data sample.

Abstract: A method performed by an annotation system for supporting annotation of objects in image frames of a traffic environment-related video sequence. The annotation system determines an annotation of an object in an image frame of the video sequence, which annotation comprises at least a first property of the object; tracks the object through the video sequence; and assigns the at least first object property to the object in one or more previous and/or subsequent image frames of the video sequence. The annotation system further identifies at least a first image frame based on one or more criterion. Moreover, the annotation system appoints the at least first identified image frame as annotation data.