Abstract: Disclosed are safety improvements for vehicles, including methods and systems of child seat detection in collaboration with a seat occupancy classification system. Corresponding systems, vehicles, and computer programs are also presented. In an aspect, a method includes receiving a current image showing a region of a vehicle seat currently captured inside the vehicle and retrieving one or more reference images, wherein at least one reference image is a previously stored first reference image of the vehicle seat being unoccupied. The method further includes determining a seat state of the vehicle seat by processing the current image and one or more reference images with a pre-trained machine learning classification network and determining, based on an output from the network, the seat state indicating at least whether the vehicle seat in the current image includes a mounted child seat. Finally, the method forwards the seat state to the seat occupancy classification system.

Abstract: Disclosed are methods, apparatuses and computer program products configured for estimating a height of a passenger seated at a vehicle seat. In an aspect, one or more images of a portion of a vehicle interior are received. The images show at least a part of the passenger seated on the vehicle seat and/or at least a part of the vehicle seat, but at least one of the images shows at least the part of the passenger seated on the vehicle seat. Based on the images, a number of body keypoints indicative of the location of defined body portions of the passenger are determined and a number of seat keypoints indicative of the location of defined points of the vehicle seat is determined. Based at least on a correlation of the determined body and seat keypoints, the height of the passenger is estimated and outputted to a seat occupancy classification system.

Abstract: 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.

Abstract: 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.

Abstract: A computerized method of determining seat occupancy of a vehicle is presented. The method comprises obtaining an image of a vehicle cabin showing at least one seat of the vehicle, determining objects in the image and assigning objects to the at least one seat, determining probabilities for seat occupancy states of the at least one seat, and determining a seat occupancy state of the at least one seat based on the assigned objects and the probabilities.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.