Abstract: A method for performing a function of a vehicle includes determining a current vehicle location of the vehicle. The method also includes determining a probability of a start of a next journey within a time window for each of a set of time windows depending on the determined current vehicle location. A departure time window is predicted for the start of the next journey, wherein the departure time window for the start of the next journey is a time window of the set of time windows for which the determined probability has the highest value. The method further includes performing the function of the vehicle if the determined probability of the predicted departure time window exceeds a predetermined threshold value.

Abstract: A computer-implemented method and system for map matching Global Navigation Satellite System (GNSS) positions of a vehicle with location information of a digital map, including map matching the determined distance between two respective detected consecutive GNSS positions of the vehicle with a route length between the two respective consecutive GNSS positions of the vehicle on the digital map.

Abstract: A method for providing a predicted current destination to a user of a vehicle includes receiving a first quantity of destinations of the user for a first period of time, and receiving a first data set of hyperparameters of a machine learning method. The method further includes training the machine learning method with the received hyperparameters using the first quantity of destinations as training data, receiving a second quantity of destinations of the user for a second period of time, predicting destinations and probabilities of the destinations, evaluating the predicted destinations and the probabilities of the predicted destinations from the second quantity of destinations with a cost function.

Abstract: A method provides a route-specific service to a user of a vehicle. The method includes receiving a set of driven navigation routes of the vehicle, and identifying navigation route clusters using the set of driven navigation routes. The method also includes training a machine learning method using the identified navigation route clusters as training data. The method further includes receiving a characteristic of a current journey of the vehicle, and predicting a probability for each identified navigation route cluster of the vehicle depending on the received characteristic, using the trained machine learning method. A navigation route cluster with the highest predicted probability is identified, and the route-specific service is provided to the user of the vehicle for the current journey if the navigation route cluster with the highest predicted probability has a probability value that exceeds a predetermined threshold.

Abstract: A method for operating a motor vehicle. A map of corresponding event locations for one or more high load events is provided, wherein the one or more high load events have historically led to an above-average load on a vehicle component. A current position and/or route of the motor vehicle is determined. A high load event is identified from among the one or more high load events as likely to be relevant to the motor vehicle during the current operation. The identification of the high load event is based on the current position and/or route of the motor vehicle. The vehicle component is thermally preconditioned, via a corresponding automatic control of at least one device of the motor vehicle, before the motor vehicle has reached the corresponding event location of the identified high load event.

Abstract: A method for providing navigation data from a backend server to a vehicle includes transmitting a request message from the vehicle to the backend server for a first quantity of navigation data, and receiving the first quantity of navigation data from the backend server in response. The first quantity of navigation data includes a quantity of decision points. The method includes determining a traversal of a decision point from the decision points by the vehicle. If the decision point has been traversed, then transmitting another request message from the vehicle to the backend server to request a second quantity of navigation data, and receiving the second quantity of navigation data from the backend server.

Abstract: A method for requesting predicted routes from a backend server by means of a vehicle includes receiving a predicted route and a quantity of decision points for the predicted route from the backend server by the vehicle. The method also includes determining a traversal of a decision point from the quantity of decision points by the vehicle. A request is submitted to provide a further predicted route and a further quantity of decision points from the vehicle to the backend server after the vehicle has traversed the decision point. The method further includes receiving the further predicted route and the further quantity of decision points from the backend server by the vehicle.

Abstract: A method for obtaining a quantity of decision points from a backend server to a vehicle during a driving process using the vehicle includes sending a first request message to request a first quantity of decision points from the vehicle to the backend server. The method also includes receiving from the background server the first quantity of decision points using the vehicle, responsive to the first request message. The method further includes determining a traversal of a decision point of the first quantity of decision points using the vehicle. If a decision point from the first quantity of decision points has been traversed, then a second request message is sent to request a second quantity of decision points from the vehicle to the backend server, and the second quantity of decision points is received from the background server using the vehicle, responsive to the second request message.

Abstract: Methods, systems, and electronic control units are provided. It is determined whether a motor vehicle has driven on a road included in digital map material. First absolute position data is captured relating to the motor vehicle using an absolute positioning system and capturing first vehicle odometry data using an odometry system of the motor vehicle at a first point in time during operation of the motor vehicle. Further absolute position data is captured relating to the motor vehicle using the absolute positioning system and capturing further vehicle odometry data using the odometry system at one further point in time during operation of the motor vehicle which differs from the first point in time. It is determined whether the motor vehicle has driven on a road included in digital map material based on the digital map material, the captured data, and a map-matching algorithm.

Abstract: A method provides a route-specific service to a user of a vehicle. The method includes receiving a set of driven navigation routes of the vehicle, and identifying navigation route clusters using the set of driven navigation routes. The method also includes training a machine learning method using the identified navigation route clusters as training data. The method further includes receiving a characteristic of a current journey of the vehicle, and predicting a probability for each identified navigation route cluster of the vehicle depending on the received characteristic, using the trained machine learning method. A navigation route cluster with the highest predicted probability is identified, and the route-specific service is provided to the user of the vehicle for the current journey if the navigation route cluster with the highest predicted probability has a probability value that exceeds a predetermined threshold.

Abstract: A method for providing a predicted current destination to a user of a vehicle includes receiving a first quantity of destinations of the user for a first period of time, and receiving a first data set of hyperparameters of a machine learning method. The method further includes training the machine learning method with the received hyperparameters using the first quantity of destinations as training data, receiving a second quantity of destinations of the user for a second period of time, predicting destinations and probabilities of the destinations, evaluating the predicted destinations and the probabilities of the predicted destinations from the second quantity of destinations with a cost function.

Abstract: A method provides a message comprising a starting time of a predicted navigation route. The method includes receiving a last vehicle position of a journey of the user using the vehicle, and determining a current vehicle location using the received last vehicle position. The method also includes predicting a departure time window for a start of a next journey, a destination of the next journey, and a probability for the start of the next journey to the predicted destination in the predicted departure time window on the basis of the determined current vehicle location. If the probability exceeds a specified threshold, then a navigation route, an estimated journey time, and a starting time are determined. Also, the message comprising the starting time of the predicted navigation route is transmitted if the starting time of the predicted navigation route corresponds to a current time.

Abstract: A method for performing a function of a vehicle includes determining a current vehicle location of the vehicle. The method also includes determining a probability of a start of a next journey within a time window for each of a set of time windows depending on the determined current vehicle location. A departure time window is predicted for the start of the next journey, wherein the departure time window for the start of the next journey is a time window of the set of time windows for which the determined probability has the highest value. The method further includes performing the function of the vehicle if the determined probability of the predicted departure time window exceeds a predetermined threshold value.

Abstract: A method determines a weather area for a motor vehicle, with which a sensor system and a communications interface are associated, which are coupled to one another via signals. The method includes: receiving data including geographical information in the form of a map; and creating a grid by dividing the received data into a plurality of adjacent grid cells. The method also includes: providing a measurement signal of the sensor system, representing a location-related local weather situation in one of the grid cells; and determining a local weather area for the respective grid cell according to the provided measurement signal.

Abstract: A method of determining a first route on a first map based on a second route on a second map, comprising the steps of determining second points on the second map, the second points being included in the second route; determining first points on the first map; wherein the first points correspond in pairs to second points; and determining the first route comprising the first points by means of trace matching with respect to the second route.

Abstract: A computer-implemented method and system for map matching Global Navigation Satellite System (GNSS) positions of a vehicle with location information of a digital map, including map matching the determined distance between two respective detected consecutive GNSS positions of the vehicle with a route length between the two respective consecutive GNSS positions of the vehicle on the digital map.

Abstract: A method for operating a motor vehicle. A map of corresponding event locations for one or more high load events is provided, wherein the one or more high load events have historically led to an above-average load on a vehicle component. A current position and/or route of the motor vehicle is determined. A high load event is identified from among the one or more high load events as likely to be relevant to the motor vehicle during the current operation. The identification of the high load event is based on the current position and/or route of the motor vehicle. The vehicle component is thermally preconditioned, via a corresponding automatic control of at least one device of the motor vehicle, before the motor vehicle has reached the corresponding event location of the identified high load event.

Abstract: Methods, systems, and electronic control units are provided. It is determined whether a motor vehicle has driven on a road included in digital map material. First absolute position data is captured relating to the motor vehicle using an absolute positioning system and capturing first vehicle odometry data using an odometry system of the motor vehicle at a first point in time during operation of the motor vehicle. Further absolute position data is captured relating to the motor vehicle using the absolute positioning system and capturing further vehicle odometry data using the odometry system at one further point in time during operation of the motor vehicle which differs from the first point in time. It is determined whether the motor vehicle has driven on a road included in digital map material based on the digital map material, the captured data, and a map-matching algorithm.

Abstract: A method for determining a map matching confidence includes detecting a trajectory; detecting network data containing a plurality of links of a network; detecting one or more data pairs, each of said one or more data pairs including a link from the plurality of links and a time window that captures at least one portion of the trajectory; determining, for each of the one or more data pairs, a map matching confidence for the link of the relevant data pair based on: determining a maximum a posteriori probability; or determining by using a modified forward algorithm, wherein the map matching confidence is designed to indicate a probability that the relevant link has been affected by the trajectory within the relevant time window.

Abstract: A method provides a functional road class of route sections of a digital map. The method regularly or continuously provides present geographic positions of a plurality of vehicles of a vehicle fleet in a central unit; determines navigation attributes for all route sections of a digital map in dependence on the geographic positions of the plurality of vehicles; and respectively assigns functional road classes to the route sections in dependence on the navigation attributes. A most probable route can be determined by joining route sections to one another, wherein starting from a respective observed route section, which initially corresponds to the geographic position of the vehicle, the route sections are expanded by adding a further route section in dependence on the functional road class.