Aptiv Technologies Limited

Aptiv Technologies Ag

Klaus Friedrichs

Monika Heift

Xuebing Zhang

Markus Buehren

Alexander Barth

Ido Freeman

Andre Paus

Thomas Kurbiel

Amil George

Markus Bühren

Computer implemented method for detecting an occupancy of a seat, comprising capturing, by means of an imaging device, a first image of a seat, the image comprising depth data, determining, by means of a processing device, a first height profile along a first line in the first image from the depth data, and comparing, by means of the processing device, the first height profile with a first reference height profile taken along the same first line to determine whether the seat is occupied.

Method and system for detecting an occupancy of a seat

Computer implemented method for detecting a type of seat occupancy, comprising capturing, by means of an imaging device, an image of a seat, the image comprising depth data and intensity data, performing, by means of a processor device, a classifier algorithm on the captured image to determine a level of occupancy, wherein, if the determination indicates that the level of occupancy is above a predetermined threshold, the method comprises processing, by means of the processor device, the depth data with a convolutional neural network, to determine a type of occupation and wherein, if the determination indicates that the level of occupancy is below a predetermined threshold, the method comprises processing, by means of the processor device, the intensity data with a convolutional neural network to determine a type of occupation.

Method and system for detecting a type of seat occupancy

The present disclosure describes a method for occupancy class prediction, such as for occupancy class detection in a vehicle. In aspects, the method includes determining, for a plurality of points of time, measurement data related to an area and determining, for a plurality of points of time, occlusion values based on the measurement data. The method further includes selecting, for a present point of time, one of a plurality of modes for occupancy class prediction based on the occlusion values for at least one of the present point of time and a previous point of time and/or based on one of the plurality of modes for occupancy class prediction selected for the previous point of time. The method additionally includes determining, for the present point of time, one of a plurality of predetermined occupancy classes of the area based on the selected mode for the present point of time.

Methods and system for occupancy class prediction and occlusion value determination

A method for classifying a capture taken by a sensor includes receiving a sequence of a plurality of captures taken by a sensor, each of the captures comprising a plurality of elements; generating, per capture, a plurality of raw probability values, each of the raw probability values being linked to a respective one of a plurality of predetermined classes and indicating the probability that the capture or an element of the capture is associated with the respective class; determining, for a respective one of the captures, a plurality of consolidated probability values in dependence of a plurality of base probability values and a plurality of context probability values, wherein the base probability values represent the raw probability values of the respective capture and the context probability values represent the raw probability values of at least one further capture of the sequence other than the respective capture, the context probability values being normalised according to a normalisation rule; and classifying the respective capture or an element thereof on the basis of the consolidated probability values.

Method for classifying a capture taken by a sensor

A device for detecting vehicle lights in an image, the device is configured to receive an image captured by a camera, wherein the image comprises an array of pixels and each pixel of the image has a respective scene brightness value, compress the image to generate a compressed image, wherein the compressed image comprises an array of pixels and each pixel of the compressed image has a respective grey value, calculate for each grey value in the compressed image a corresponding scene brightness value in the image, and detect vehicle lights in the compressed image by using the calculated scene brightness values.

Growing community of inventors

Dortmund, Germany

Klaus Friedrichs