Computer implemented method for controlling the movement of a host vehicle, the method comprising: determining lane data related to road items (132, 135, 136) from a data source and object data acquired via a sensor system from objects, wherein the road items (132, 135, 136) and the objects are located in an external environment of the host vehicle, feeding a machine-learning algorithm with the lane data and the object data to generate a set of controlling functions for controlling the movement of the vehicle by performing the steps of: classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle, and controlling the movement of the host vehicle based on the classified maneuvers and the determined controlling functions, wherein for training the machine-learning algorithm, common ground truth data is applied to classify the predefined set of maneuvers and to determine the controlling functions, the common ground truth data is acquired as data recorded for the host vehicle over a predetermined time period, additional pre-classified data is generated by pre-classifying the predefined set of maneuvers using the data recorded for the host vehicle, and the additional pre-classified data is applied for the training of determining the controlling functions. the method comprising: determining lane data related to road items (132, 135, 136) from a data source and object data acquired via a sensor system from objects, wherein the road items (132, 135, 136) and the objects are located in an external environment of the host vehicle, feeding a machine-learning algorithm with the lane data and the object data to generate a set of controlling functions for controlling the movement of the vehicle by performing the steps of: classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle, and controlling the movement of the host vehicle based on the classified maneuvers and the determined controlling functions, determining lane data related to road items (132, 135, 136) from a data source and object data acquired via a sensor system from objects, wherein the road items (132, 135, 136) and the objects are located in an external environment of the host vehicle, feeding a machine-learning algorithm with the lane data and the object data to generate a set of controlling functions for controlling the movement of the vehicle by performing the steps of: classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle, and classifying a predefined set of maneuvers for the host vehicle based on the lane data and the object data, and determining a set of controlling functions related to kinematics of the host vehicle, and controlling the movement of the host vehicle based on the classified maneuvers and the determined controlling functions, wherein for training the machine-learning algorithm, common ground truth data is applied to classify the predefined set of maneuvers and to determine the controlling functions, the common ground truth data is acquired as data recorded for the host vehicle over a predetermined time period, additional pre-classified data is generated by pre-classifying the predefined set of maneuvers using the data recorded for the host vehicle, and the additional pre-classified data is applied for the training of determining the controlling functions. for training the machine-learning algorithm, common ground truth data is applied to classify the predefined set of maneuvers and to determine the controlling functions, the common ground truth data is acquired as data recorded for the host vehicle over a predetermined time period, additional pre-classified data is generated by pre-classifying the predefined set of maneuvers using the data recorded for the host vehicle, and the additional pre-classified data is applied for the training of determining the controlling functions.