Abstract: In a method for driving maneuver assistance of a vehicle, a predefined neural network is provided, which is designed to determine whether a predefined driving maneuver is probably possible. A predefined driver model is provided, which is designed to predict a probable future behavior of a vehicle. A current driving situation of the vehicle is determined. Depending on the determined driving situation, the driver model and the neural network, it is determined whether a predefined driving maneuver is possible. Depending on the determination as to whether the driving maneuver is possible, a driver assistance function for the driving maneuver is carried out and/or the driving maneuver is carried out autonomously.

Abstract: A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

Abstract: A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

Abstract: In a method for driving maneuver assistance of a vehicle, a predefined neural network is provided, which is designed to determine whether a predefined driving maneuver is probably possible. A predefined driver model is provided, which is designed to predict a probable future behavior of a vehicle. A current driving situation of the vehicle is determined. Depending on the determined driving situation, the driver model and the neural network, it is determined whether a predefined driving maneuver is possible. Depending on the determination as to whether the driving maneuver is possible, a driver assistance function for the driving maneuver is carried out and/or the driving maneuver is carried out autonomously.

Abstract: A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

Abstract: An assessment of whether a roadway is directional is provided. The assessment includes stipulating a dimension if not previously calculated, and receiving of an output from an apparatus for environment recognition for a vehicle, which output is based on recognition of an environment of the vehicle at one instant. The assessment also include associating a factor with the output, wherein the factor is based on a speed of the vehicle at the instant associated with the output. The output is mapped into a rating value in accordance with a predefined mapping specification, and the factor is associated with the rating value. The dimension is then calculated based on a sum that is based on the rating value weighted with the associated factor, and on the previously calculated or stipulated dimension, after which the dimension can be provided.

Abstract: An assessment of whether a roadway is directional is provided. The assessment includes stipulating a dimension if not previously calculated, and receiving of an output from an apparatus for environment recognition for a vehicle, which output is based on recognition of an environment of the vehicle at one instant. The assessment also include associating a factor with the output, wherein the factor is based on a speed of the vehicle at the instant associated with the output. The output is mapped into a rating value in accordance with a predefined mapping specification, and the factor is associated with the rating value. The dimension is then calculated based on a sum that is based on the rating value weighted with the associated factor, and on the previously calculated or stipulated dimension, after which the dimension can be provided.