Abstract: A computer-implemented method is provided for determining a navigation route between a start point and a target point. The method includes setting the start and target points, and then generating a road map with a road graph having plural edges between the star and target points representing traversable route portions. The generated road map also has nodes that interconnect the adjacent edges. The road map is generated from geospatial data retrievably stored in a database. The method proceeds by generating an emotion map layer by a machine learning model that is trained to predict, based on static and/or dynamic contextual input data for each of the edges, the likely emotions of a driver as they traverse the relevant edge and generate emotion weightings in the emotion map layer. The method then calculates the navigation route by the emotion map layer with the emotion weightings obtained in the previous step.

Abstract: A computer-implemented method for determining an emotional state of a person in a motor vehicle. The method includes the following steps: recording (S1) environmental data using environmental sensors of the motor vehicle, the environmental data relating to an environment of the motor vehicle; recognizing (S2) various objects using the environmental data; assigning (S3) the objects to various classes; determining (S4), respectively, a number of the objects assigned to the respective classes; and determining (S5) the emotional state using the numbers and the classes.

Abstract: A method for robust identification of a vehicle occupant. Simultaneously acquired data pairs from interior sensors and sensing elements are used as a training data set for global learning of a Deep Canonical Correlation Analysis architecture with two neural networks, wherein an authentication of the respective vehicle occupants from the group of vehicle users is carried out in a first step as an initialization. In a second step or a subsequent use, only one of the two sensing elements is then needed to identify the respective vehicle occupant. Also described is a system, via which the robust identification is provided by way of the method.

Abstract: A method for producing a model (15) for automated prediction of interactions of a user with a user interface of a motor vehicle. Vehicle operating logs (11, 12, 13) are provided and each includes a record of a time sequence of user interactions with the user interface. Each of the user interactions recorded in the vehicle operating logs (11, 12, 13) is assigned context information (21, 22) that includes a functional category (21) of the user interaction and a driving state (22) of the motor vehicle at the time of the user interaction. Training data (14) are generated based on the vehicle operating logs (11, 12, 13) and the associated context information (21, 22). A context-sensitive interaction model (15) is trained by machine learning on the basis of the training data (14) to make a prediction about a future user interaction based on a time sequence of past user interactions.

Abstract: A system for a motor vehicle includes a sensor apparatus having a sensor for determining motor vehicle data and/or driving data of the motor vehicle, and an evaluation unit. The evaluation unit includes an emotion determination unit configured to assess the emotions of the driver of the motor vehicle on the basis of sensor signals transmitted by the sensor.