Volkswagen Ag

Audi Ag

Valeo Schalter Und Sensoren Gmbh

Hella Gmbh & Co Kgaa

Stephan Max

Peter Baumann

Claudia Prausse

Mark Beckmann

Thomas Niemann

Marc-Michael Meinecke

Stefan Brosig

Sascha Bauer

Thorben Günzel

Alexander Urban

Reiner Katzwinkel

Edson William Fangwa Nzeya

Kristian Weiß

Sven Klomp

Christian Tendyck

Jesko Klandt

Mehmet Eylem Kirlangic

Andreas Wege

Lars Krüger

Torsten Büschenfeld

Hans Wilhelm Wehling

Oliver Eidel

Timur Aminev

Ulrich Wuttke

Frank Bärecke

Uwe Kupfernagel

Robert Konnopka

Kristian Weiss

Thomas Jung

Christian Jördens

Andreas Titze

Hans-Wilhelm Wehling

Martin Wegner

Volkmar Tanneberger

Michael Rohlfs

Stefan Triebl

Dominique Nemetschek

Sabina Alazzawi

Richard Niestroj

Kai Franke

Sönke Freter

Joerg Weyland

Hubert Neft

Natalie Weber

Stefan Bosse

Thomas Grummel

Pedro Almeida

Markus Stinner

Thomas Günterberg

Christian Alexander Seidel

Stefan Kollmorgen

Klaas Hauke Baumgärtel

Torsten Bueschenfeld

Björn Richter

Olgierd Zaleski

Toralf Hessel

Andreas Bockstedte

Vladislavs Serebro

Sebastian Hamel

Roman Koch

Mirko Steinberg

Claudia Prauße

Artur Skrobanski

Jörg Weyland

Christopher Prohm

Pablo Grube Krebs

Torsten BÜSCHENFEDLD

Technologies and techniques for ascertaining a vehicle trajectory, via a system that includes a motor vehicle and a network server. Individual trajectories of a plurality of motor vehicles being traversed, and secondary information linked to the individual trajectories are detected. The individual trajectories of the plurality of motor vehicles, and the linked secondary information may be transmitted to a network server. A swarm trajectory may be ascertained by a network server as an average of the individual trajectories using the individual trajectories and the linked secondary information, wherein the average may be weighted using the secondary information. The network server may transmit the swarm trajectory to at least one motor vehicle. An associated motor vehicle configured for the system and network server is further disclosed.

Method for ascertaining a vehicle trajectory

The disclosure relates to a method, to a computer program with instructions, and to a device for processing data recorded by a motor vehicle. The disclosure additionally relates to a motor vehicle and to a back end in which a method according to the disclosure or a device according to the disclosure is used. In a first step, an item of data recorded by a motor vehicle is received. A temporal obfuscation is then applied to the received item of data. In the process, a temporally segmented obfuscation interval is used for the temporal obfuscation. The obfuscated item of data is finally forwarded for further processing. The temporal obfuscation can be carried out within the motor vehicle or in a back end connected to the motor vehicle.

Method, computer program, and device for processing data recorded by a motor vehicle

The disclosure relates to a method for processing data recorded by a motor vehicle. In a first step, at least one set of parameters for a temporal and spatial obfuscation of the recorded data is received. An item of data recorded by a motor vehicle is additionally received. A temporal and spatial obfuscation is then applied to the received item of data using the at least one received set of parameters. The obfuscated item of data is finally forwarded for further processing. The obfuscation may take place within the motor vehicle or in a back end connected to the motor vehicle.

Method, computer program, and device for processing data recorded by a motor vehicle, and for providing parameters for such processing

A method for collecting transportation vehicle-based data and transferring the data to a backend computer, wherein the respective data sets relate to predefined route sections travelled along by a swarm of data-collecting vehicles, wherein parameterized orders for the acquisition of transportation vehicle-based data are stored in the backend computer and each order includes the parameter of route section, which defines the route section for which transportation vehicle-related data is to be collected; the parameter of swarm size, which defines how many transportation vehicles per unit of time are to collect data for the specified route section; and the parameter time interval, which defines the time interval in which data is to be acquired for the route section. Route-related data is continuously transferred to the backend computer from each transportation vehicle of the swarm, wherein a header constitutes an overview of the transportation vehicle-based data stored temporarily in the transportation vehicle.

Method for collection of transportation vehicle-based, location-related data records

A method for a transportation vehicle of a transportation vehicle fleet for transmitting data to a data processing system including receiving a message, wherein the message includes information about data to be transmitted and information about a predetermined probability of the data transmission; determining whether the data to be transmitted are transmitted from the transportation vehicle to the data processing system, using a random number generator and the information about the predetermined probability of the data transmission; and sending the data to be transmitted to the data processing system, in response to determining that the data to be transmitted are transmitted from the transportation vehicle to the data processing system.

Method for a transportation vehicle of a transportation vehicle fleet for transmitting data to a data processing system, method for a data processing system for transmitting data of a transportation vehicle fleet to the data processing system, and transportation vehicle

Technologies and techniques for anonymizing vehicle data. A data set is created on the basis of captured vehicle data and includes information regarding the location and/or time of the data capture. The information may be anonymized on the basis of traffic flow data by locally concealing the information regarding the location of the data capture and/or by temporally concealing the information regarding the time of the data capture. The traffic flow data may be based on group information received by the vehicle providing the vehicle data via vehicle-to-vehicle communication from other vehicles. A related motor vehicle and a network server directed to the anonymization is also provided herein.

Method for anonymizing vehicle data

Technologies and techniques for quality determination for a traffic system. First traffic parameter data is acquired from the traffic system, indicative of a parameter of the traffic system. Second traffic parameter data is acquired, indicative of another parameter of the traffic system, wherein the second traffic parameter data originate from a plurality of traffic participants. The parameter from the first traffic parameter data and second traffic parameter are obtained, wherein the parameter obtained from the first traffic parameter data is compared to the parameter obtained from the second traffic parameter data in order to determine the quality of the first traffic parameter data.

Quality determining method, quality determining device, and motor vehicle

A method and a device for acquiring, offering, and transmitting vehicle-based data of a route section, in particular environment data acquired by a vehicle of a vehicle swarm traveling on the route section and offered to a back-end computer and transmitted if required, comprising: reporting available vehicle-based data relating to the route section currently being traveled on to the back-end computer; requesting at least one selection of the vehicle-based data offered relating to the predefined route section from the vehicle by means of the back-end computer; collecting the requested selection of the vehicle-based data by means of the sensors of the vehicle and storing the data in a memory the vehicle; and transmitting the requested and collected data to the back-end computer.

Collecting of vehicle-based, location-specific data set

Technologies and techniques for monitoring and correcting the obfuscation of vehicle-related data. Personal data may be removed from the transmitted vehicle-related data, and the vehicle-related data after the removal of the personal references may be combined to form a vehicle-related data set without personal references. A temporal and spatial obfuscation of the vehicle-related data set may be carried out without personal references based on an estimated size of a cluster of data-collecting vehicles for generating an anonymous data set. The anonymous data set, including the degree of obfuscation, may be provided to a data user and an actual cluster size for a spatial region based on the provided anonymous data sets may be determined and compared with the estimated cluster size used for the obfuscation of the anonymous data set. The comparison result may be then used to correct the obfuscation of the spatial region.

Monitoring and correcting the obfuscation of vehicle related data

Technologies and techniques for determining a cluster-based stopping point for a motor vehicle for a predefined reason for stopping in a lane of a road. The individual stopping points of a plurality of vehicles may be determined for the reason for stopping in the lane, wherein the vehicles are controlled by individual drivers. A distribution of the individual stopping points in the lane at least in the direction of travel of the vehicles may be determined. The maximum of the distribution may be determined and stored as a cluster-based stopping point. The cluster-based stopping point determined in this way may be applied to autonomous driving technologies.

Determination and use of cluster-based stopping points for motor vehicles

A method for assisting a driver of a vehicle with driving of the vehicle is disclosed, in which the surroundings of the vehicle are detected. The detection of the surroundings comprises the course of driving paths as well as movement data relating to other road users. Historic data obtained at least from past driving maneuvers by road users in the detected surroundings are called up for the surroundings, the detected surroundings of the vehicle are analyzed and a traffic situation of the vehicle in relation to at least one second road user of the other detected road users is determined. A likely driving maneuver of the second road user is predicted based on the called-up historic data and the current traffic situation, and a control signal for assisting the driver with driving of the vehicle is generated depending on the predicted driving maneuver of the second road user.

Method and driver assistance system for assisting a driver of a vehicle with driving of the vehicle

A method for calibrating at least one position sensor in a vehicle, comprising: saving a map in the vehicle with at least one item of virtual location information from the environment of a predefined calibration route, moving the vehicle on the predefined calibration route, detecting at least one item of actual location information via the at least one position sensor while the vehicle moves on the predefined calibration route, carrying out a comparison between the at least one item of virtual location information and the at least one item of actual location information, calculating a map-based movement of the vehicle within the saved map based on the comparison, calculating an odometry-based movement of the vehicle, and calibrating the at least one position sensor based on a comparison between the map-based movement and the odometry-based movement.

Method for calibrating a position sensor in a vehicle, computer program, storage means, control unit and calibration route

The present invention relates to a method, to a computer program comprising instructions and to a device for determining a vehicle spacing for an observation period. The invention also relates to a motor vehicle and to a back end, in which a method according to the invention, or device according to the invention, is used. In a first step, measurement values relating to an average vehicle spacing for a plurality of measurement times are received. These values may be stored for later use. The situation may arise in which an unusable measurement value is identified at an error time. In this case, a vehicle spacing for a current observation period that contains the error time is determined based on a vehicle spacing determined from the recorded measurement values for a previous observation period.

Method, computer program and device for determining a vehicle spacing for an observation period

Technologies and techniques for anonymously providing data of a motor vehicle. A first dataset is generated by a motor vehicle, and the first dataset is anonymized using a vehicle computing unit. User related data and the anonymized first dataset are communicated to a first server system using the vehicle computing unit and the communicated user related data is deleted using the first server system. The anonymized first dataset is communicated to a second server system using the first server system after deletion of the user related data.

Providing data of a motor vehicle

A method and a device for detecting odor nuisances along a route of a subject transportation vehicle and for predictively initiating measures for preventing the penetration of the odorants of the odor nuisance into the subject transportation vehicle during travel. The method includes collecting swarm data from a plurality of swarm transportation vehicles in the surroundings of the route of the subject transportation vehicle, relating to the odor nuisance of each swarm transportation vehicle, ascertaining locations of an odor nuisance from the collected swarm data, ascertaining the extent of the odor nuisance around the respective location of the odor nuisance, and initiating measures in the subject transportation vehicle when approaching the location of an odor nuisance while taking into account the extent of the odor nuisance along the future route, wherein the measure is implemented at a predefined distance from the extent of the odor nuisance.

Method and device for detecting odor in a transportation vehicle

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Gifhorn, Germany

Stephan Max