Abstract: A computer-implemented method for operating a vehicle includes capturing an interior of the vehicle by optical sensors, capturing a condition of the vehicle by vehicle sensors, mapping optical data determined by the optical sensors and vehicle data determined by the vehicle sensors to indicators by an artificial neural network, wherein the indicators indicate at least a probability of a correct grip of a driver of the vehicle on the steering wheel of the vehicle, outputting the plurality of indicators, controlling the vehicle depending on the indicators output, wherein the artificial neural network is semantically divided into at least a first section and a second section, and wherein in the first section only the optical data are processed and in the second section data provided by the first section are processed together with the vehicle data.

Abstract: A method for operating a system includes a transportation vehicle and a server, wherein swarm data including data sets are provided on the server, each data set representing a path and a speed associated with the current location of the vehicle, wherein a swarm path and speed are determined based on the swarm data, wherein an identifier associated with the driver of the vehicle is determined, wherein, depending on the identifier, a target path deviation from the swarm path and/or a target speed deviation from the swarm speed is determined, wherein a target path for the vehicle is determined based on the target path deviation and the swarm path, and/or wherein a vehicle target speed determined based on the target speed deviation and the swarm speed, wherein the vehicle is automatically guided longitudinally and/or transversely depending on the target speed and/or the target path using a driver assistance system.

Abstract: A method for operating a system that includes a motor vehicle and a server, wherein swarm data includes data sets provided on the server, each data set representing a first variable, relating to longitudinal dynamics, and/or a second variable relating to a driven path, wherein the data sets are allocated to subgroups depending on their first variable or on their second variable, wherein a predefined driver profile is selected based on an input, wherein one of the subgroups is assigned to the selected driver profile, wherein a first control variable for a driver assistance system for automatic longitudinal guidance and/or automatic transverse guidance is determined based on the first variables and/or second variables of those data sets allocated to the subgroup assigned to the selected driver profile, wherein the motor vehicle is automatically longitudinally and/or transversely guided using the driver assistance system depending on the first control variable.

Abstract: A method for longitudinal guidance of a vehicle relative to a leading vehicle driving in front by using an electronic distance-keeping system, includes providing an environmental parameter relating to an environment of the vehicle, providing a target distance at which the vehicle is intended to follow the leading vehicle, providing acceleration and/or braking information relating to the leading vehicle, assigning the leading vehicle to one of at least two different, predefined driving behavior classes based on the environmental parameter and the acceleration and/or braking information, and determining an at least temporarily permitted deviation from the target distance based on the assigned driving behavior class. The distance-keeping system is used to at least temporarily longitudinally guide the vehicle as following the leading vehicle based on the target distance and the permitted deviation. A distance-keeping system for a vehicle is also provided.

Abstract: Technologies and techniques for controlling a vehicle, in which geometric test data and driving dynamics test data are obtained on a test track. The test data are divided into clusters, and cluster-specific drive-dynamic data is defined for each cluster. Subsequently, the route to be driven is clustered and the vehicle is controlled in a route section in accordance with the specified drive-dynamic data of the respective cluster. Under such configurations, it may be ensured that the driver is supported as individually as possible when driving on unknown routes.

Abstract: A method and device for recognizing a hands-off state at a vehicle steering wheel. At least one steering variable is detected at the steering wheel and supplied as input data to a trained machine learning model. The model comprises a trained main model and a preceding trained adapter model. The adapter model determines general input data from specific input data. The main model recognizes the hands-off state from the general input data and outputs associated state information. This approach allows adapting the machine learning model to various usage conditions with reduced effort by only modifying the adapter model while keeping the main model fixed.

Abstract: A method for recognizing a hands-off state at a steering wheel of a vehicle includes detecting at least one steering variable at the steering wheel, detecting, determining, and/or querying at least one piece of context information, and supplying the detected steering variable and context information as input data to a trained machine learning model. The model is configured to recognize a hands-off state based on at least the detected steering variable and context information and to output an associated piece of state information. The steering variable and context information may be processed by separate components of the model. Additionally, or alternatively, at least one component of the model that processes the steering variable may be initialized based on the context information. A device for recognizing a hands-off state at a steering wheel is also provided.

Abstract: Technologies and techniques for laterally guiding a motor vehicle include a device with at least one assistance system, at least one surroundings sensor system, and/or access to a database of swarm data. The device is configured to detect driving lane boundaries based on data from the surroundings sensor system and/or swarm data. The assistance system generates steering instructions for a steering system, based on the data, to maintain the motor vehicle on a target trajectory. The motor vehicle driver may adapt the target trajectory, and the assistance system generates steering instructions to maintain the adapted target trajectory. The device identifies areas, referred to as operational design domains (ODDs), where the motor vehicle driver is permitted to remove their hands from the steering handle. A sensor system detects whether hands are removed. If no hands are detected, or upon entering an ODD, the system transitions to a predefined target trajectory.

Abstract: A method for detecting a hands-off state at a steering wheel. A steering variable is captured at the steering wheel and the captured steering variable is supplied as input data to at least two trained machine learning models. The machine learning models are trained to detect a hands-off state on the basis of at least the captured steering variable and to output it as output data. The output data are combined in weighted form to form a hands-off state and are provided. The values of the weightings are determined on the basis of a current context. There is also described an apparatus for detecting a hands-off state at a steering wheel, a steering system for a vehicle, and a vehicle with the apparatus for detecting the hands-off state.

Abstract: A method for longitudinal guidance of a vehicle relative to a leading vehicle driving in front by using an electronic distance-keeping system, includes providing an environmental parameter relating to an environment of the vehicle, providing a target distance at which the vehicle is intended to follow the leading vehicle, providing acceleration and/or braking information relating to the leading vehicle, assigning the leading vehicle to one of at least two different, predefined driving behavior classes based on the environmental parameter and the acceleration and/or braking information, and determining an at least temporarily permitted deviation from the target distance based on the assigned driving behavior class. The distance-keeping system is used to at least temporarily longitudinally guide the vehicle as following the leading vehicle based on the target distance and the permitted deviation. A distance-keeping system for a vehicle is also provided.

Abstract: The disclosure relates to a method for detecting hand contact with a steering wheel of a vehicle, wherein a decision, based on at least detected and/or received status data of a steering system of the vehicle, is made by means of at least one trained machine learning method as to whether at least one hand is in contact with the steering wheel or not, wherein an actuation of at least one control element arranged on the steering wheel is taken into account as an input variable of the at least one trained machine learning method, and wherein a decision signal is generated and provided.

Abstract: A method for estimating a steering wheel torque for a mechanical feedback at a steering wheel of a steering system of a motor vehicle is disclosed, comprising: receiving and/or detecting at least one current measurement value of at least one state variable of the motor vehicle by suitable input means; estimating the current steering wheel torque by means of a controller; outputting the estimated steering wheel torque as a steering wheel torque signal, wherein the estimation is carried out by means of a parameterizable steering model, wherein current measurement values of at least a subset of the at least one state variable are fed as input data to the parameterizable steering model for this purpose, and wherein nonlinearities of the steering system are estimated based on the current measurement values of at least the subset of the at least one state variable using a method of artificial intelligence.

Abstract: Technologies and techniques for controlling a vehicle, in which geometric test data and driving dynamics test data are obtained on a test track. The test data are divided into clusters, and cluster-specific drive-dynamic data is defined for each cluster. Subsequently, the route to be driven is clustered and the vehicle is controlled in a route section in accordance with the specified drive-dynamic data of the respective cluster. Under such configurations, it may be ensured that the driver is supported as individually as possible when driving on unknown routes.

Abstract: The invention relates to a method for preparing or generating a training data set for machine learning to operate a vehicle component. Provided multidimensional data points are divided up in a first step by dividing up the plurality of data points into multidimensional clusters by using a cluster algorithm. Then a training data set is generated by selecting data points from the basic training data set. The selection comprises determining a smallest cluster among the plurality of clusters with the lowest number of data points. Furthermore, at least one subset of the data points of the smallest cluster is provided for the training data set. In another step, a subset of data points is selected from each of the other clusters for the training data set, wherein the number of selected data points of each other cluster corresponds to the number of selected data points of the smallest cluster.

Abstract: A method for performing an at least partially automatic vehicle function of a vehicle depending on a travel route to be assessed by means of a driver assistance system is disclosed. The method comprises providing a plurality of clusters from route data with respect to at least one known travel route, wherein the clusters group the route data sectionwise according to predefined geometric parameters. The method comprises providing recorded course data that indicate a course of the travel route to be assessed and applying the clusters to the course data in order to divide the travel route to be assessed into route sections corresponding to the clusters. The method comprises determining at least one uncertainty quantity which is characteristic of an uncertainty with respect to the assignment made and determining a control quantity as a function of the uncertainty quantity and providing the control quantity for performing the vehicle function.

Abstract: A method for estimating a steering wheel torque for a mechanical feedback at a steering wheel of a steering system of a motor vehicle is disclosed, comprising: receiving and/or detecting at least one current measurement value of at least one state variable of the motor vehicle by suitable input means; estimating the current steering wheel torque by means of a controller; outputting the estimated steering wheel torque as a steering wheel torque signal, wherein the estimation is carried out by means of a parameterizable steering model, wherein current measurement values of at least a subset of the at least one state variable are fed as input data to the parameterizable steering model for this purpose, and wherein nonlinearities of the steering system are estimated based on the current measurement values of at least the subset of the at least one state variable using a method of artificial intelligence.

Abstract: The invention relates to a method for preparing or generating a training data set for machine learning to operate a vehicle component. Provided multidimensional data points are divided up in a first step by dividing up the plurality of data points into multidimensional clusters by using a cluster algorithm. Then a training data set is generated by selecting data points from the basic training data set. The selection comprises determining a smallest cluster among the plurality of clusters with the lowest number of data points. Furthermore, at least one subset of the data points of the smallest cluster is provided for the training data set. In another step, a subset of data points is selected from each of the other clusters for the training data set, wherein the number of selected data points of each other cluster corresponds to the number of selected data points of the smallest cluster.