Bayerische Motoren Werke Aktiengesellschaft

Peter Pedron

Heidrun Belzner

Sebastian Gruenwedel

Pascal Minnerup

Sebastian Rauch

Maxi Winter

Manus Mcelhone

Marc Scott Meissner

Martin Zeman

Barbara Roessle

A computer-implemented method estimates a vehicle position. The method provides a first position estimate based on position information from at least one first source, wherein the first position estimate is assigned a first bias value and a first statistical variance; provides a second position estimate based on position information from at least one second source; determines a third bias value and a third statistical variance, wherein the third bias value and the third statistical variance are assigned to a third position estimate which results from a combination of the first position estimate and the second position estimate; and evaluates the first position estimate and the third position estimate on the basis of a quality criterion which takes into account the bias value assigned to each position estimate and the statistical variance assigned to each position estimate. When, according to the quality criterion, the third position estimate is better than the first position estimate, the third position estimate is used as the result of the estimation of the vehicle position or as the basis for a combination with position information from one or more additional sources. When, according to the quality criterion, the first position estimate is better than the third position estimate, the first position estimate is used as the result of the estimation of the vehicle position or as the basis for a combination with position information from one or more additional sources.

Computer-implemented method for estimating a vehicle position

A method for processing measurement data of a vehicle for determining the start of a search for a parking space includes acquiring journey data vectors, each containing information about a speed, position data and a time of acquisition of the speed and the position data, and establishing a feature vector at each time of acquisition of the journey data vectors, where the information about a current and a previous journey data vector is processed, and where the feature vector comprises as feature components at least one item of speed information and one item of path information. The method also includes classifying each feature vector, where a first traffic category, which represents a vehicle journey, or a second vehicle category, which represents parking space-seeking traffic, is assigned to each feature vector, and where a probability is established, which specifies the probability with which the first or the second traffic category is to be assigned to the feature vector. In addition, the method includes segmenting the feature vectors over the time profile of the established traffic categories, where there is a subdivision of the journey from the start to the last acquisition of a journey data vector into two segments in accordance with the determined traffic categories of the feature vectors and the transition from one segment into the other segment represents the start of the search for a parking space.

Method for processing measurement data of a vehicle in order to determine the start of a search for a parking space

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Augsburg, Germany

Peter Pedron