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: Alkyl 2-acetamido-2-deoxy-D-glucopyranoside uronic acids, and their pharmaceutically or cosmetically acceptable salts, and their use as surface-active and/or interfacially active active compounds, in particular as detergent surfactants or emulsifiers.

Abstract: Use of polysaccharides for increasing the light protection factor of cosmetic or dermatological preparations which comprise at least one customary UV filter substance.

Abstract: Alkyl 2-acetamido-2-deoxy-D-glucopyranoside uronic acids, and their pharmaceutically or cosmetically acceptable salts, and their use as surface-active and/or interfacially active active compounds, in particular as detergent surfactants or emulsifiers.

Abstract: Alkyl 2-acetamido-2-deoxy-D-glucopyranoside uronic acids, and their pharmaceutically or cosmetically acceptable salts, and their use as surface-active and/or interfacially active active compounds, in particular as detergent surfactants or emulsifiers.

Abstract: Cosmetic or pharmaceutical preparations, characterized by an effective content of pharmaceutically and/or cosmetically acceptable glycoglycerolipids.

Abstract: Use of flavone derivatives and flavanone derivatives, in particular flavonoids for stabilizing cosmetic or dermatologically acceptable substances, the chemical formula of which includes the structural moiety of dibenzoylmethane, against the decomposition caused by UV radiation.

Abstract: Cosmetic ceutical formulations characterized by an effective, content of pharmaceutically and/or cosmetically unobjectionable hexosyl glycerides and/or (hexosyl)hexosyl glycerides.

Abstract: The present invention is directed to a new process for the production of chitin oligomers, including fragmentation, partial hydrolysis and recovery. This process produces high yields, especially of higher (tetra- to deca-) chitin oligomers.