Abstract: A method and system for fusing image data from an image acquisition sensor. The method includes: a) receiving input image data including: a first image which having a first region of a scene, and a second image which includes a second region of the scene, wherein the first and second regions overlap one another but are not identical; b) determining first and second feature maps using the first and second images, respectively; c) computing first and second output feature maps by convolutions of the first and second feature maps, respectively; d) computing a fused feature map through element-by-element addition of the first and second output feature maps, wherein the relative positions of the first and second regions are utilized, such that elements in the region of overlap are added. The method is runtime-efficient and fuses image data from one or more image acquisition sensors for an ADAS/AD vehicle system.

Abstract: A method and system for fusing data from at least one sensor, including: receiving input sensor data, wherein the input sensor data include: first and second representations including first and second regions, respectively, of a scene, wherein the first and second regions overlap one another but are not identical; determining first and second feature maps on the basis of the first and second representations, respectively; computing first and second output feature maps by a convolution of the first and second feature maps, respectively; and computing a fused feature map through element-by-element addition of the first and second output feature maps, wherein the relative position of the first and second regions to one another is used, such that the elements in the region of overlap are added; and outputting the fused feature map. The method is runtime-efficient and deployed to fuse data from environment sensors for a vehicle's ADAS/AD system.

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 device is configured to classify data. Its operation involves providing (210) data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated (220) by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor.

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: Various embodiments of the teachings herein include a method for training and updating a backend-side classifier comprising: receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier; and training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation.

Abstract: A method for the three-dimensional graphic or pictorial reconstruction of a vehicle begins with capturing an image of at least one vehicle with a camera. A first rectangular border of the entire vehicle is captured in the image, to obtain a first rectangle. A second rectangular border of one side of a vehicle is captured in the image, to obtain a second rectangle, and it is determined whether the first and second rectangles are borders which relate to the same vehicle. If so, then it is determined whether a side orientation of the vehicle can be assigned from the two rectangles and, if so, the side orientation is determined. Finally, a three-dimensional reconstruction of the vehicle is performed from the first rectangle, the second rectangle and the side orientation.

Abstract: Various embodiments of the teachings herein include a method for training and updating a backend-side classifier comprising: receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier; and training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation.

Abstract: A camera device includes an optronics system and an image capture control unit, for acquiring a sequence of images of a surrounding region of a vehicle. The optronics system includes a wide-angle optical system and a high-resolution image acquisition sensor. The optronics system and the image capture control unit are configured to generate a reduced-resolution binned image of the entire capture region of the optronics system, or to capture an unbinned high-resolution image of a subregion of the capture region, respectively for each individual image of the sequence of images, depending on a current traffic and/or surrounding situation. A height and a width of the subregion are set depending on the current situation. A size of the subregion is set such that the pixel count of the high-resolution image of the subregion is no greater than the pixel count of the reduced-resolution image of the entire capture region.

Abstract: A method for the three-dimensional graphic or pictorial reconstruction of a vehicle begins with capturing an image of at least one vehicle with a camera. A first rectangular border of the entire vehicle is captured in the image, to obtain a first rectangle. A second rectangular border of one side of a vehicle is captured in the image, to obtain a second rectangle, and it is determined whether the first and second rectangles are borders which relate to the same vehicle. If so, then it is determined whether a side orientation of the vehicle can be assigned from the two rectangles and, if so, the side orientation is determined. Finally, a three-dimensional reconstruction of the vehicle is performed from the first rectangle, the second rectangle and the side orientation.

Abstract: A device is configured to classify data. Its operation involves providing (210) data samples including one or more of: image data, radar data, acoustic data, and/or lidar data to a processing unit. The data samples include at least one test sample, including positive samples and negative samples. Each positive sample has been determined to contain data relating to at least one object to be detected including at least one pedestrian, car, vehicle, truck or bicycle. Each negative sample has been determined not to contain data relating to the at least one object to be detected. These determinations regarding the positive samples and the negative samples are provided as input data, validated by a human operator, and/or provided by the device itself through a learning algorithm. A first plurality of groups is generated (220) by the processing unit implementing an artificial neural network, wherein at least some of the first plurality of groups are assigned a weighting factor.

Abstract: The invention relates to a camera device for capturing a surrounding region of an ego-vehicle. The camera device comprises an optronics system and an image capture control unit, which are configured to acquire a sequence of images of the surrounding region. The optronics system comprises a wide-angle optical system and a high-resolution image acquisition sensor.

Abstract: An object-recognition method for a vehicle's driver assistance system involves obtaining a 2D image and a 3D image, forming a 3D apparent object from the 3D image, detecting one or more detected objects in a portion of the 2D image corresponding to the apparent object from the 3D image, classifying the one or more detected objects into at least one pre-defined object class, and dividing the apparent object into at least two 3D objects when the apparent object does not correspond with at least one class-specific property of the determined at least one object class.

Abstract: The invention relates to a method and a device for detecting objects. The method comprises the following steps: forming at least one object from a depth image of a 3D camera evaluating and classifying the one or more objects (2, 3) in a 2D image (5) which corresponds to the at least one formed object in the depth image (1), possibly dividing the at least one object (1) formed from the depth image into a plurality of individual objects (2, 3) while taking into account the classification of the one or more objects (2, 3) in the 2D image (5). The method and the device can be used in particular within the framework of a driver assistance system for detecting objects in a vehicle environment.

Abstract: A method for regulating the velocity of a motor vehicle in a complex traffic situation is presented. The motor vehicle is equipped with a sensor system for recording the environment. In order to regulate the velocity, an at least partially covered object and/or at least one object on an adjacent lane is viewed.

Abstract: The invention relates to a method for determining a speed of a vehicle (1), whereby at least two images (A, B) of an area surrounding the vehicle are recorded in time succession using a camera on the vehicle (1), and between the images (A, B) changes (2) in a position and/or a size of at least one object (X) contained in the images (X) are determined, and from the changes, a speed of the vehicle (1) is determined relative to the object (X).

Abstract: A method for regulating the velocity of a motor vehicle in a complex traffic situation is presented. The motor vehicle is equipped with a sensor system for recording the environment. In order to regulate the velocity, an at least partially covered object and/or at least one object on an adjacent lane is viewed.

Abstract: The invention relates to a method for determining a speed of a vehicle (1), whereby at least two images (A, B) of an area surrounding the vehicle are recorded in time succession using a camera on the vehicle (1), and between the images (A, B) changes (2) in a position and/or a size of at least one object (X) contained in the images (X) are determined, and from the changes, a speed of the vehicle (1) is determined relative to the object (X)

Abstract: The invention provides diagnostic methods, kits, and systems, and related computer-readable media, which use multiple blood marker values, including serum and plasma marker values, to aid in the diagnosis of the status or progress of a liver disease in a patient. The invention also provides methods and systems, and related computer-readable media, that use blood marker values, including serum and plasma marker values: (1) to screen for active ingredients useful in the treatment of a liver disease; (2) to aid in the selection of treatment regimens for patients that are predisposed to, or suffer from, liver disease; and (3) to aid in the design of clinical programs useful in monitoring the status or progress of liver disease in one or more patients.

Abstract: The invention provides a casting car for use in removing super-heated molten materials from an electric arc melting furnace. The cooling car has a metal frame which includes a shallow container adapted to receive the molten materials. A relatively permanent refractory lining protects the interior surface of the container, and a replenishable, granular refractory material is deposited over the permanent refractory lining. Heat deflectors extend upwardly from the metal frame about the top of the container in order to channel heat radiated from the interior upwardly whereby melting plant equipment and personnel located near the cooling car are not subjected to very intense radiant heat. Wheels are secured to the bottom of the metal frame to permit the cooling car to be rolled along the track leading to the melting furnace to receive the super-heated molten materials.