Cariad Se

Car.software Estonia As

Volkswagen Ag

Michael Holicki

Roland Kube

Timo Iken

Stefan Wappler

Ralph Hänsel

Carolin Last

Christian Jördens

Jonas Hoffmann

Thomas Dammeier

Sven-Garrit Czarnian

A method is described in which a position of a vehicle is determined in a road network, having a plurality of edges, each of them being associated with a road section or a driving lane on a road section, by means of a sensor group of the vehicle, wherein first sensor data are detected by means of the sensor group, on the basis of the first sensor data each edge of a plurality of first edges of the road network is associated with a first probability that the vehicle is located in the road section associated with the first edge or in the driving lane associated with the first edge, second sensor data are detected by means of the sensor group, on the basis of the second sensor data and the probabilities associated with the first edges, a second probability is associated with each edge of a plurality of second edges of the road network that the vehicle is located in the road section associated with the second edge or in the driving lane associated with the second edge, and the second edge with the highest associated probability is determined to be the position of the vehicle.

Method and device for determining a position of a vehicle in a road network

According to a method for self-localization of a vehicle, a first pose of the vehicle is determined in a map coordinates system, based on environment sensor data representing an environment of the vehicle, a landmark is detected in the environment, a position of the landmark is determined in the map coordinates system and a second pose of the vehicle is determined in the map coordinates system dependent on the position of the landmark. An assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type. Depending on the assignment instruction, a first environment sensor system is activated and a second environment sensor system is deactivated, whereupon the environment sensor data are generated by means of the first environment sensor system.

Self-localization of a vehicle in a parking infrastructure with selective sensor activation

According to a method for self-localization of a vehicle, a first pose of the vehicle is determined. Environment sensor data are generated by means of an environment sensor device and on the basis of this a landmark is detected in the environment. A position of the landmark is determined and in dependence on this a second pose of the vehicle is determined. A memorized assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type, and depending on this a first portion of the environment sensor data is selected that was generated by means of a first environment sensor system of the environment sensor device configured according to a first sensor type. For the detecting of the landmark, a first landmark detection algorithm is applied to the first portion of the environment sensor data.

Self-localization of a vehicle in a parking infrastructure

A method for determining a position of a mobile unit with at least one sensor device, which is set up to capture environmental images of the mobile unit, and with a map, which comprises at least known landmarks with map position information. At least one hypothesis is determined for the at least one initially estimated position of the mobile unit and the at least one landmark detected in an environmental image, wherein the at least one hypothesis comprises at least one association of the at least one detected landmark of the at least one captured environmental image with at least one known landmark of the map. At least one new estimate of the position of the mobile unit and a weight of the at least one hypothesis are determined for the at least one hypothesis by means of at least one Kalman filter.

Localization of a mobile unit by means of a multi hypothesis kalman filter method

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Berlin, Germany

Michael Holicki