Abstract: The invention relates to a method for detecting beacons (4) in the surroundings of an ego vehicle (5), comprising the steps of: capturing (S1) a sequence of camera images (1) of a section of the surroundings by means of a-camera system (6) of the ego vehicle (5), detecting (S2) bright light spots in the recorded camera image (1), cutting out (S3) regions (3) containing the detected bright light spots in the camera image (1), classifying (S4) the cut-out regions (3), classing (S5) the bright light spots as beacons (4) if the classified bright light spots are detected repeatedly and therefore a threshold value of a counter is exceeded.

Abstract: Example embodiments relate to an ADAS sensor data processing unit, to an ADAS sensor system and to an ADAS sensor data evaluation method for use in driver assistance systems or systems for the automated driving of a vehicle. The ADAS sensor data processing unit includes an input interface, a decompression module, a processing unit and an output unit. The input interface is designed to receive data of an ADAS sensor that have been subjected to lossy compression by a compression module. The decompression module is designed to decompress the compressed data of the ADAS sensor. The processing unit is designed to process the decompressed data (IdSD) of the ADAS sensor, information relevant to an ADAS/AD function being ascertained from the decompressed sensor data. The output unit is designed to output the ascertained information relevant to the ADAS function.

Abstract: The disclosure relates to a method and system for extracting disparity information out of a pair of stereo camera images for processing images of camera sensors for Automated Driving or Advanced Driver Assistance Systems in a vehicle. The method includes: a) receiving a pair of unrectified stereo camera images, b) performing a 2D-matching of the received pair of images using a trained convolutional neural network and providing the 2D-displacement on a pixel-by-pixel level as disparity information, and c) outputting the 2D-displacement on pixel level. The method is robust which does not require a high precision rectification. Applying rectification on the disparity values after the matching process leads to reduced algorithmic complexity with simultaneous preservation of all information.

Abstract: A method and a driver assistance system recognize the intention of a pedestrian to move on the basis of a sequence of images of a camera. The method includes detecting a pedestrian in at least one camera image. The method also includes selecting a camera image that is current at the time t and selecting a predefined selection pattern of previous camera images of the image sequence. The method further includes extracting the image region in which the pedestrian was detected in the selected camera images of the image sequence. The method also includes classifying the movement profile of the detected pedestrian on the basis of the plurality of extracted image regions. The method outputs the class that describes the movement intention determined from the camera images of the image sequence.

Abstract: The present disclosure relates to a method and system for recognizing road users, in particular persons in road traffic The method includes the following steps: recording at least one ambient image or a sequence of ambient images by means of an optical sensor of a vehicle, detecting a person in the ambient image, determining a pose of the detected person; determining a presence of a means of transport of the person; and assigning a movement profile to the person based on the determined means of transport of the person.

Abstract: A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Abstract: Example embodiments relate to an ADAS sensor data processing unit, to an ADAS sensor system and to an ADAS sensor data evaluation method for use in driver assistance systems or systems for the automated driving of a vehicle. The ADAS sensor data processing unit includes an input interface, a decompression module, a processing unit and an output unit. The input interface is designed to receive data of an ADAS sensor that have been subjected to lossy compression by a compression module. The decompression module is designed to decompress the compressed data of the ADAS sensor. The processing unit is designed to process the decompressed data (IdSD) of the ADAS sensor, information relevant to an ADAS/AD function being ascertained from the decompressed sensor data. The output unit is designed to output the ascertained information relevant to the ADAS function.

Abstract: A method for detecting the presence of an emergency vehicle includes receiving a plurality of image frames over a period of time, determining an EV colour component for each image frame based on the ratio of a first colour relative to the total colour in each pixel and assigning to a pixel a first value if the EV colour component exceeds a predefined threshold value and a second value if the EV colour component does not, and determining for an EV colour value for the first colour based on the sum of all of the first values for each image frame. The method also includes generating a time domain representation, converting the time domain representation for the plurality of image frames to a frequency spectrum, and determining if any flashing light sources of the first colour associated with one or more types of emergency vehicles is present.

Abstract: An apparatus for detecting a traffic light phase for a motor vehicle, includes: an image sensor device (10), configured to capture an image of a traffic light and to provide the image as image data; a segmentation device (20) configured to define at least one partial region of the captured image and to assign it to at least one signal lamp of the traffic light; a scaling device (30), configured to determine a maximum color saturation and/or a maximum brightness in the at least one partial region; and a computer device (40) configured to determine a signal status of the traffic light based on the maximum color saturation and/or the maximum brightness.

Abstract: A method and a driver assistance system recognize the intention of a pedestrian to move on the basis of a sequence of images of a camera. The method includes detecting a pedestrian in at least one camera image. The method also includes selecting a camera image that is current at the time t and selecting a predefined selection pattern of previous camera images of the image sequence. The method further includes extracting the image region in which the pedestrian was detected in the selected camera images of the image sequence. The method also includes classifying the movement profile of the detected pedestrian on the basis of the plurality of extracted image regions. The method outputs the class that describes the movement intention determined from the camera images of the image sequence.

Abstract: The invention relates to a method for detecting beacons (4) in the surroundings of an ego vehicle (5), comprising the steps of: capturing (S1) a sequence of camera images (1) of a section of the surroundings by means of a-camera system (6) of the ego vehicle (5), detecting (S2) bright light spots in the recorded camera image (1), cutting out (S3) regions (3) containing the detected bright light spots in the camera image (1), classifying (S4) the cut-out regions (3), classing (S5) the bright light spots as beacons (4) if the classified bright light spots are detected repeatedly and therefore a threshold value of a counter is exceeded.

Abstract: A method of ascertaining disparities in sensor data uses at least one neural network implemented in a controller of a vehicle. The method involves capturing (101) a learning data record from temporally successive raw sensor data, evaluating (102) the learning data record to train the neural network exclusively based on the learning data record of the captured raw sensor data, ascertaining (103) expected sensor data, comparing (104) the ascertained expected sensor data with sensor data currently captured by the sensor arrangement, and ascertaining (105) a disparity between the currently captured sensor data and the ascertained expected sensor data.

Abstract: A method for detecting the presence of an emergency vehicle includes receiving a plurality of image frames over a period of time, determining an EV colour component for each image frame based on the ratio of a first colour relative to the total colour in each pixel and assigning to a pixel a first value if the EV colour component exceeds a predefined threshold value and a second value if the EV colour component does not, and determining for an EV colour value for the first colour based on the sum of all of the first values for each image frame. The method also includes generating a time domain representation, converting the time domain representation for the plurality of image frames to a frequency spectrum, and determining if any flashing light sources of the first colour associated with one or more types of emergency vehicles is present.

Abstract: An apparatus for detecting a traffic light phase for a motor vehicle, includes: an image sensor device (10), configured to capture an image of a traffic light and to provide the image as image data; a segmentation device (20) configured to define at least one partial region of the captured image and to assign it to at least one signal lamp of the traffic light; a scaling device (30), configured to determine a maximum color saturation and/or a maximum brightness in the at least one partial region; and a computer device (40) configured to determine a signal status of the traffic light based on the maximum color saturation and/or the maximum brightness.