Abstract: A method for generating training data for a machine learning model. The method includes: providing LIDAR point clouds, each of which is assigned to a point in time of a plurality of successive points in time, wherein each point of each LIDAR point cloud represents a particular object class of a plurality of object classes; for each LIDAR point cloud: ascertaining a transmission grid map in spherical coordinate space, wherein each voxel of the transmission grid map indicates how many rays pass through the voxel before they are reflected at a point in the LIDAR point cloud; ascertaining a reference transmission grid map in Cartesian coordinate space assigned to a reference point in time of the plurality of points in time; for each of the plurality of object classes: for each LIDAR point cloud, ascertaining a reflection grid map associated with the object class.

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 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.