Abstract: 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.

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: Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

Abstract: A computer-implemented method for collision threat assessment of a vehicle includes obtaining context information for the surrounding of the vehicle, including information about at least one road user. The method includes determining ego occupancy information for multiple possible future locations of the vehicle at multiple future points in time based on the context information. The method includes determining road user occupancy information for multiple possible future locations of the at least one road user at multiple future points in time based on the context information. The method includes fusing the ego occupancy information and the road user occupancy information to obtain fused occupancy information at each future point in time. The method includes determining a collision threat value based on the fused occupancy information.

Abstract: Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

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: 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.

Abstract: A computer implemented method for determining a command of an occupant of a vehicle comprises the following steps carried out by computer hardware components: determining object information indicating information about at least one object outside the vehicle; determining occupant gesture information indicating information related to the occupant; and selecting a task to be carried out based on the object information and the occupant gesture information.