Abstract: A computer-implemented method for mixture distribution estimation of multi-modal future predictions comprising a training phase of a convolutional neural network comprising the steps of: (1) inputting a set of images of a driving environment, each containing at least one object of interest, and a set of future ground truths corresponding to the objects of interest; (2) sampling the solution space of the multi-modal future of the object of interest with an evolving winner-takes-all loss strategy by generating a predetermined number of hypotheses, penalizing all hypotheses equally, gradually releasing one part of the hypotheses by penalizing only the other part of the hypotheses being closer to the corresponding ground truth, so-called winning hypotheses, until only the best hypothesis being the closest one is penalized, and outputting final hypotheses; (3) sequentially fitting a multi-modal mixture distribution of future predictions to the final hypotheses.

Abstract: A computer-implemented method for multimodal egocentric future prediction in a driving environment of an autonomous vehicle (AV) or an advanced driver assistance system (ADAS) equipped with a camera and comprising a trained reachability prior deep neural network (RPN), a trained reachability transfer deep neural network (RTN) and a trained future localization deep neural network (FLN) and/or a trained future emergence prediction deep neural network (EPN).

Abstract: A computer-implemented method for mixture distribution estimation of multi-modal future predictions comprising a training phase of a convolutional neural network comprising the steps of: (1) inputting a set of images of a driving environment, each containing at least one object of interest, and a set of future ground truths corresponding to the objects of interest; (2) sampling the solution space of the multi-modal future of the object of interest with an evolving winner-takes-all loss strategy by generating a predetermined number of hypotheses, penalizing all hypotheses equally, gradually releasing one part of the hypotheses by penalizing only the other part of the hypotheses being closer to the corresponding ground truth, so-called winning hypotheses, until only the best hypothesis being the closest one is penalized, and outputting final hypotheses; (3) sequentially fitting a multi-modal mixture distribution of future predictions to the final hypotheses.

Abstract: A method for creating a neural network, which includes an encoder that is connected to a decoder. The optimization method DARTS is used, a further cell type being added to the cell types of DARTS. A computer program and a device for carrying out the method, and a machine-readable memory element, on which the computer program is stored, are also described.