Computer-implemented method for training an artificial neural network, ANN, capable of multitasking, wherein the ANN is configured to process images supplied to the ANN for the execution of a plurality of different tasks by means of the ANN, wherein the plurality of different tasks comprises at least a first task and a second task, wherein the first task is traffic sign recognition and the second task is a semantic scene segmentation, wherein the ANN comprises an input layer, at least one cross-task intermediate layer and a plurality of corresponding task-specific ANN sections, wherein each task-specific ANN section functions as the output layer of the ANN for the task corresponding to this ANN section, comprising the following steps: - providing a first path for a first information flow through the ANN, wherein the first path couples the input layer of the ANN to the least one cross-task intermediate layer of the ANN, and the first path couples the at least one cross-task intermediate layer to each task-specific ANN section from the plurality of corresponding task-specific ANN sections, - supplying first training data for training cross-task parameters, which are common to the plurality of mutually different tasks of the ANN, via the input layer and the first path, - providing at least one task-specific, second path for a second information flow, different from the first information flow, through the ANN, wherein the second path couples the input layer of the ANN to only one task-specific ANN section from the plurality of task-specific ANN sections, wherein the task for which one task-specific ANN section functions as the output layer of the ANN is the task or the task, and - supplying second training data and training only task-specific parameters via the second path on the basis of the second training data for training the ANN for that task for which one task-specific ANN section functions as the output layer of the ANN. wherein the ANN is configured to process images supplied to the ANN for the execution of a plurality of different tasks by means of the ANN, wherein the plurality of different tasks comprises at least a first task and a second task, wherein the first task is traffic sign recognition and the second task is a semantic scene segmentation, wherein the ANN comprises an input layer, at least one cross-task intermediate layer and a plurality of corresponding task-specific ANN sections, wherein each task-specific ANN section functions as the output layer of the ANN for the task corresponding to this ANN section, comprising the following steps: - providing a first path for a first information flow through the ANN, wherein the first path couples the input layer of the ANN to the least one cross-task intermediate layer of the ANN, and the first path couples the at least one cross-task intermediate layer to each task-specific ANN section from the plurality of corresponding task-specific ANN sections, - supplying first training data for training cross-task parameters, which are common to the plurality of mutually different tasks of the ANN, via the input layer and the first path, - providing at least one task-specific, second path for a second information flow, different from the first information flow, through the ANN, wherein the second path couples the input layer of the ANN to only one task-specific ANN section from the plurality of task-specific ANN sections, wherein the task for which one task-specific ANN section functions as the output layer of the ANN is the task or the task, and - supplying second training data and training only task-specific parameters via the second path on the basis of the second training data for training the ANN for that task for which one task-specific ANN section functions as the output layer of the ANN. - providing a first path for a first information flow through the ANN, wherein the first path couples the input layer of the ANN to the least one cross-task intermediate layer of the ANN, and the first path couples the at least one cross-task intermediate layer to each task-specific ANN section from the plurality of corresponding task-specific ANN sections, - supplying first training data for training cross-task parameters, which are common to the plurality of mutually different tasks of the ANN, via the input layer and the first path, - providing at least one task-specific, second path for a second information flow, different from the first information flow, through the ANN, wherein the second path couples the input layer of the ANN to only one task-specific ANN section from the plurality of task-specific ANN sections, wherein the task for which one task-specific ANN section functions as the output layer of the ANN is the task or the task, and - supplying second training data and training only task-specific parameters via the second path on the basis of the second training data for training the ANN for that task for which one task-specific ANN section functions as the output layer of the ANN.