Abstract: A method for providing data for creating a digital map. The method includes: detecting surroundings sensor data of the surroundings during a measuring run of a physical system, preferably a vehicle, the surroundings sensor data capturing the surroundings in an at least partially overlapping manner, first surroundings sensor data including three-dimensional information, and second surroundings sensor data including two-dimensional information; extracting, with the aid of a first neural network situated in the physical system, at least one defined object from the first and second surroundings sensor data into first extracted data; and extracting, with the aid of a second neural network situated in the physical system, characteristic features including descriptors from the first extracted data into second extracted data, the descriptors being provided for a defined alignment of the second extracted data in a map creation process.

Abstract: A control unit for a vehicle for analyzing localization systems, the control unit being connectable in a data-conducting manner to at least two localization systems which are operable independently of one another for ascertaining system-specific positions, each localization system including at least one sensor, the control unit being configured to evaluate pieces of position information ascertained by the localization systems by subjecting them to a plausibility check. Also described are a related method and a sensor system.

Abstract: A method and device for determining a first highly precise position of a vehicle. The method includes acquiring surrounding-area data values using at least one radar sensor of the vehicle, the surrounding-area data values representing a surrounding area of the vehicle; and determining a rough position of the vehicle as a function of the acquired surrounding area data values. In addition, the method includes determining surrounding-area feature data values as a function of the determined rough position of the vehicle, the surrounding-area feature data values representing at least one surrounding-area feature and a second highly precise position of the at least one surrounding-area feature; and determining the first highly precise position of the vehicle as a function of the at least one surrounding-area feature, according to predefined localization criteria, the first highly precise position of the vehicle being more precise than the rough position of the vehicle.

Abstract: A method and a corresponding system for localizing a vehicle using a digital map are described. In accordance with the method, the digital map assigns to the features one or a plurality of predetermined attributes, which are provided for characterizing possibly occurring actual changes in the features comparison to the digital map existing at the moment. On the basis of the assigned attributes, probabilities relative to the changes are also determined, and the vehicle is localized in consideration of the determined probabilities.

Abstract: A method for utilizing observed locally customary behavior from historical data sets in vehicle surroundings by a control unit. Historical data sets of vehicle trajectories of at least one first vehicle and/or at least one second vehicle are received. A locally customary behavior of the first vehicle and/or of the second vehicle is ascertained from the historical data sets. A registered behavior of the first vehicle, of at least one third vehicle or a digital map being checked for deviations based on the ascertained locally customary behavior. When a deviation of the locally customary behavior from a registered behavior of the first vehicle, a registered behavior of a third vehicle, or a deviation of the locally customary behavior from the digital map is established, the execution of at least one function is initiated. A control unit, a computer program, and a machine-readable memory medium, are also described.

Abstract: A method for certifying map elements for safety-critical driving functions by a control unit, at least one observation variable of at least one mapping step being ascertained by at least one map element after an implementation of the mapping step by a monitoring function, and being compared with a setpoint value of the observation variable, at least one result value being calculated based on a comparison of the observation variable with the setpoint value of the observation variable for the at least one mapping step by the monitoring function, the at least one result value being stored as a certificate and being linked with the at least one map element. A control unit, a computer program as well as a machine-readable memory medium are also described.

Abstract: A method is described for the certification by a control unit of map elements for safety-critical driving functions. At least one observation variable of at least one mapping step of at least one map element is ascertained after an implementation of the mapping step via a monitoring function and is compared with a setpoint value of the observation variable. At least one result value is calculated based on a comparison of the observation variable with the setpoint value of the observation variable for the at least one mapping step via the monitoring function. The at least one result value is stored as a certificate and is linked to the at least one map element. A control unit, a computer program and a machine-readable memory medium are also described.

Abstract: A method for fusing state data via a control unit. State data of a first mobile unit and of an object ascertained via a sensor system of the first mobile unit are received. State data of an object ascertained via a sensor system of a second mobile unit and/or state data of the second mobile unit, transmitted via a communication link from the second mobile unit to the first mobile unit, are received. A node is created in a time-position diagram for each set of received state data of the first mobile unit, the second mobile unit, and the objects. A data optimization of the state data ascertained by the first mobile unit and/or by the second mobile unit is carried out. An optimization problem is created based on the optimized state data ascertained by the first mobile unit and the optimized state data received from the second mobile unit.

Abstract: A method and a device for determining a highly precise position of a vehicle. The method includes a step of sensing environment data values, which represent an environment of the vehicle, the environment encompassing multiple environment features which have at least one regular structure, and the environment data values at least encompassing the at least one regular structure; a step of carrying out a comparison of the environment data values with a map; a step of determining the highly precise position of the vehicle as a function of the comparison; and a step of supplying a signal on the basis of the highly precise position.

Abstract: A method for locating a highly automated vehicle (HAV) in a digital location map, including: providing a digital map in a driver assistance system of the HAV; determining a current vehicle position, and locating the vehicle position in the digital map; identifying a route segment currently traveled by the HAV in the digital map; providing at least one traveled comparison trajectory of at least one additional vehicle along the currently traveled route segment; comparison of the at least one comparison trajectory with the currently traveled route segment as indicated in the digital map, and ascertaining a difference value as a result of the comparison; and ascertaining an up-to-dateness of the currently traveled route segment in the digital map, at least partly on the basis of the difference value.

Abstract: A method for operating a control device of a motor vehicle driving by automation. The method includes determining a location of the motor vehicle, and acquiring driving-environment data of the motor vehicle, a control characteristic of the control device of the motor vehicle being formed in such a way that a driving behavior of at least one other road user is influenced in defined manner.

Abstract: A method for ascertaining features in an environment of at least one mobile unit for implementation of a localization and/or mapping by a control unit. In the course of the method, sensor measurement data of the environment are received, the sensor measurement data received are transformed by an alignment algorithm into a cost function and a cost map is generated with the aid of the cost function, a convergence map is generated based on the alignment algorithm. At least one feature is extracted from the cost map and/or the convergence map and stored, the at least one feature being provided in order to optimize a localization and/or mapping. A control unit, a computer program, and a machine-readable storage medium are also described.

Abstract: A method and a device are described for operating an automated vehicle including determining a coarse position of the automated vehicle, determining first environment data values as a function of the coarse position, the first environment data values representing a target environment of the automated vehicle, recording second environment data values using an environment sensor system of the automated vehicle, the second environment data values representing an actual environment of the automated vehicle, determining a highly accurate position of the automated vehicle, as a function of a comparison between the actual environment and the target environment, and operating the automated vehicle, as a function of the highly accurate position.

Abstract: A method for training a neural convolutional network for determining, with the aid of the neural convolutional network, a localization pose of a mobile platform using a ground image. Using a first multitude of aerial image training cycles, each aerial image training cycle includes: providing a reference pose of the mobile platform; and providing an aerial image of the environment of the mobile platform in the reference pose; using the aerial image as an input signal of the neural convolutional network; determining the respective localization pose with the aid of an output signal of the neural convolutional network; and adapting the neural convolutional network to minimize a deviation of the respective localization pose determined using the respective aerial image from the respective reference pose.

Abstract: A method and a device for controlling a mapping method. The method includes a step of detecting a position of a mapping vehicle, a step of detecting vehicle data values as a function of the position, the vehicle data values representing vehicles in surroundings of the mapping vehicle, a step of determining a quality of the surroundings for the mapping method as a function of the vehicles in the surroundings, and a step of providing a signal for controlling the mapping method as a function of the quality.

Abstract: A computer-implemented method for supplying radar data. The method includes the following steps: receiving input data, the input data including satellite images; generating radar data using a trained machine learning algorithm, which is applied to the input data; and outputting the generated radar data.

Abstract: A method for determining a localization pose of an at least partially automated mobile platform, the mobile platform being equipped to generate ground images of an area surrounding the mobile platform, and being equipped to receive aerial images of the area surrounding the mobile platform from an aerial-image system. The method includes: providing a digital ground image of the area surrounding the mobile platform; receiving an aerial image of the area surrounding the mobile platform; generating the localization pose of the mobile platform with the aid of a trained convolutional neural network, which has a first trained encoder convolutional-neural-network part and a second trained encoder convolutional-neural-network part.

Abstract: A method and a device for evaluating the contents of a map, the map containing at least one first driving environment feature, including a step of recording at least one second driving environment feature by at least one sensor of at least one vehicle, a step of comparing the at least one first driving environment feature contained in the map to the at least one second, recorded driving environment feature, and a step of evaluating the contents of the map as a function of the performed comparison.

Abstract: A method and device for creating a localization map, including a step of receiving environmental data values, a step of dividing the environmental data values into at least two separate data records, a step of creating a plurality of partial maps, a step of creating the localization map and a step of making the localization map available.

Abstract: A method for creating a map based on a base map and as a function of at least one luminous surroundings feature, as well as a method and device for operating a vehicle as a function of a feature classification of a luminous surroundings feature.