Aptiv Technologies Limited

Aptiv Technologies Ag

Monika Heift

Klaus Friedrichs

Andre Paus

Xuebing Zhang

Alexander Barth

Markus Buehren

Amil George

Thomas Kurbiel

Markus Bühren

André Paus

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 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.

Device and a method for detecting vehicle lights in an image

A device for controlling a headlight of a vehicle is configured to receive images consecutively captured by a camera, the images showing an area in front of a vehicle, detect a road in one of the images, divide the road detected in the image in segments, track the segments over time in the images, determine at least one feature related to the brightness for each of the segments, determine from the features related to the brightness of the segments whether preceding and/or oncoming traffic exists, and generate a control signal for controlling a headlight of the vehicle if preceding and/or oncoming traffic exists.

Device and a method for controlling a headlight of a vehicle

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Wuppertal, Germany

Monika Heift