Vehicle having an at least partly automated operating mode and having a recommendation system comprising a data collection module (2.1), a prediction module (2.2) and a recommendation module (2.3), the data collection module (2.1) being configured to collect vehicle data (D-FZG), surroundings data (D-UMG) and/or environment data (D-UMW); the prediction module (2.2) being configured to read in a driver profile from a set of driver profiles, each driver profile comprising a machine learning model or heuristic model, which is trained individually for each driver profile and is configured to read in the vehicle data (D-FZG), surroundings data (D-UMG) and/or environment data (D-UMW) at least for a route portion ahead and to output a predictive indication value (IND-PRÄ) as an output variable, the predictive indication value (IND-PRÄ) being a numerical value; and the recommendation module (2.3) being configured to compare the predictive indication value (IND-PRÄ) with an indication threshold value (IND-SW) and to cause a recommendation to be output in the vehicle for a person driving the vehicle to take over manual vehicle control if the predictive indication value (IND-PRÄ) is in a first range compared with the indication threshold value (IND-SW) and to cause a recommendation to be output in the vehicle for a driver assistance system to take over at least partly automated vehicle control if the predictive indication value (IND-PRÄ) is in a second range compared with the indication threshold value (IND-SW), characterized in that the recommendation module (2.3) is further configured to adapt the level of the indication threshold value (IND-SW) depending on a driver profile read in by the prediction module (2.2), the vehicle data (D-FZG), the surroundings data (D-UMG) and/or the environmental data (D-UMW). the data collection module (2.1) being configured to collect vehicle data (D-FZG), surroundings data (D-UMG) and/or environment data (D-UMW); the prediction module (2.2) being configured to read in a driver profile from a set of driver profiles, each driver profile comprising a machine learning model or heuristic model, which is trained individually for each driver profile and is configured to read in the vehicle data (D-FZG), surroundings data (D-UMG) and/or environment data (D-UMW) at least for a route portion ahead and to output a predictive indication value (IND-PRÄ) as an output variable, the predictive indication value (IND-PRÄ) being a numerical value; and the recommendation module (2.3) being configured to compare the predictive indication value (IND-PRÄ) with an indication threshold value (IND-SW) and to cause a recommendation to be output in the vehicle for a person driving the vehicle to take over manual vehicle control if the predictive indication value (IND-PRÄ) is in a first range compared with the indication threshold value (IND-SW) and to cause a recommendation to be output in the vehicle for a driver assistance system to take over at least partly automated vehicle control if the predictive indication value (IND-PRÄ) is in a second range compared with the indication threshold value (IND-SW), characterized in that the recommendation module (2.3) is further configured to adapt the level of the indication threshold value (IND-SW) depending on a driver profile read in by the prediction module (2.2), the vehicle data (D-FZG), the surroundings data (D-UMG) and/or the environmental data (D-UMW).