Abstract: A method for operating a higher-level automated vehicle (HAV), in particular a highly automated vehicle, is provided, including: S1 for providing a digital map, which may be a highly accurate digital map, in a driver assistance system of the HAV; S2 for determining an instantaneous vehicle position and localizing the vehicle position in the digital map; S3 for providing an expected setpoint traffic density at the vehicle position; S4 for ascertaining an instantaneous actual traffic density in the surroundings of the HAV; S5 for comparing the actual traffic density to the setpoint traffic density and ascertaining a difference value as the result of the comparison; S6 for checking the vehicle position of the HAV for plausibility at least partially based on the difference value and/or S7 for updating the digital map at least partially based on the difference value. Also described are a corresponding driver assistance system and a computer program.

Abstract: A method for providing operating data for an at least semiautomated vehicle. The method includes: sensorially detecting surroundings data with the aid of a surroundings sensor system of the vehicle; conveying the sensorially detected surroundings data to a cloud and conveying a request to the cloud for localization data; receiving the localization data from the cloud; and ascertaining the operating data for use in a cooperative driving maneuver of the vehicle with at least one further road user from the localization data received from the cloud and from the sensorially detected surroundings data; and initiating the cooperative driving maneuver using the ascertained operating data.

Abstract: A method and a device for operating an automated vehicle. The method includes a step of determining a rough position, a step of detecting an operating state of the automated vehicle, a step of checking whether it is possible, proceeding from the rough position, to determine a precise position as a function of the operating state, a step of determining the precise position when it is possible to determine the precise position, or a step of providing a signal, which represents a non-determination of the precise position, when it is not possible to determine the precise position, and a step of operating the vehicle as a function of the precise position or as a function of the signal.

Abstract: In a method for operating a vehicle, a planning map and a localization map are provided, the vehicle is localized on the localization map, a map corridor indicated in the planning map is selected based on the localization, a sensor corridor is ascertained using a sensor unit of the vehicle, and the map corridor is compared to the sensor corridor. Using a specified threshold value for a deviation between the map corridor and the sensor corridor, it is decided whether the map corridor and the sensor corridor are identical. The map corridor is utilized for operating the vehicle if the map corridor and the sensor corridor are identical, and the sensor corridor is utilized for operating the vehicle if the map corridor and the sensor corridor are not identical.

Abstract: A method for creating a first map. The method includes providing a second map, the second map including at least one predefined path; receiving map data, the map data representing at least one trajectory and at least one further object; and creating the first map starting from the map data, an alignment based on a superimposition of the at least one predefined path and the at least one trajectory being carried out, and subsequently a displacement of the at least one further object being carried out starting from the alignment. A device for carrying out the method for creating a first map is also described.

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 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 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 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 predicting a construction-related driving-route change of a driving route for a vehicle includes at least a step of reading in and a step of detecting. In the step of reading in, at least one image signal is read in, which represents an image, recorded by the vehicle, of a construction-site parameter in the environment of the driving route. In the step of detecting, the impending driving-route change is detected using the image signal.

Abstract: A method for operating a more highly automated vehicle (HAF), in particular a highly automated vehicle, including: S1—providing a digital map, which may be a highly accurate digital map, in a driver assistance system of the HAF; S2—providing at least one reference measuring point that is geodetically measuring in a highly accurate manner, positional data of the reference measuring point being stored in the digital map; S3—determining a current reference vehicle position by using the reference measuring point; S4—determining a current satellite vehicle position by using a global satellite navigation system (GNSS); and S5—comparing the reference vehicle position with the satellite vehicle position and ascertaining a GNSS error as a result of the comparison. Also described is a corresponding system and a computer program.

Abstract: A method for operating a higher-level automated vehicle (HAV), in particular a highly automated vehicle, is provided, including: S1 for providing a digital map, which may be a highly accurate digital map, in a driver assistance system of the HAV; S2 for determining an instantaneous vehicle position and localizing the vehicle position in the digital map; S3 for providing an expected setpoint traffic density at the vehicle position; S4 for ascertaining an instantaneous actual traffic density in the surroundings of the HAV; S5 for comparing the actual traffic density to the setpoint traffic density and ascertaining a difference value as the result of the comparison; S6 for checking the vehicle position of the HAV for plausibility at least partially based on the difference value and/or S7 for updating the digital map at least partially based on the difference value. Also described are a corresponding driver assistance system and a computer program.

Abstract: A method is described for verifying a digital map of a higher-level automated vehicle (HAV), in particular a highly automated vehicle, including the steps: S1 providing a digital map, preferably a highly accurate digital map: S2 determining an instantaneous reference position and localizing the reference position in the digital map; S3 establishing at least one actual feature property of a feature in the surroundings of the reference position, the establishment being carried out with the aid of at least one information source; S4 comparing the actual feature property to a setpoint feature property of the feature and ascertaining at least one difference value as the result of the comparison. A corresponding device and a computer program are also described.

Abstract: In a method for operating a vehicle, a planning map and a localization map are provided, the vehicle is localized on the localization map, a map corridor indicated in the planning map is selected based on the localization, a sensor corridor is ascertained using a sensor unit of the vehicle, and the map corridor is compared to the sensor corridor. Using a specified threshold value for a deviation between the map corridor and the sensor corridor, it is decided whether the map corridor and the sensor corridor are identical. The map corridor is utilized for operating the vehicle if the map corridor and the sensor corridor are identical, and the sensor corridor is utilized for operating the vehicle if the map corridor and the sensor corridor are not identical.

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 in 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, a processing unit which carries out the method, and a system including the processing unit for updating a digital map for locating motor vehicles are provided. In the case of the method for updating a digital map for locating motor vehicles, surroundings information is detected by a vehicle for the purpose of updating the digital map and the detected surroundings information is compared to the surroundings information which is stored on the digital map. In this case, the vehicle is located on the digital map, which means that the location or position of the vehicle on the digital map is known. For the purpose of detecting the surroundings information, the vehicle is navigated along a first route, whose most recently carried out comparison of surroundings information dates back the longest as compared to at least one further optional route.

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.