Abstract: A radar system for detecting the environment of a motor vehicle includes an antenna assembly comprising plastic and including one or more individual antennas for transmitting and/or receiving radar signals. A circuit board includes at least one area that is permeable by radar waves. At least one high-frequency component is coupled to one side of the circuit board and includes at least one radiating element for direct emission or receipt of radar waves in the direction of the circuit board in the least one area that is permeable by radar waves. The antenna assembly is disposed on the other side of the circuit board opposite the at least one high-frequency component. The antenna assembly includes a coupling/decoupling point disposed in the at least one area of the circuit board permeable by radar waves.

Abstract: A motor control apparatus for a three-phase brushless DC motor. The motor control apparatus comprises a power converter which has, for each phase of the brushless DC motor, an electrical half-bridge having two electronic switches, and an application-specific integrated circuit for actuating the electronic switches of the power converter. The application-specific integrated circuit can be controlled by a programmable control unit in a normal mode, has a first interface for receiving an error signal signaling a malfunction and is designed to change the power converter to a defined final operating state independently of the programmable control unit after receiving the error signal.

Abstract: The invention relates to a method and a device for fusion of measurements from various information sources (I 1, I 2, . . . , I m) in conjunction with filtering of a filter vector, wherein the information sources (I 1, I 2, . . . , I m) comprise one or more environment detection sensor(s) of an ego vehicle, wherein in each case at least one measured quantity derived from the measurements is contained in the filter vector, wherein the measurements from at least one individual information source (I 1; I 2; . . . , I m) are mapped nonlinearly to the respective measured quantity, wherein at least one of these mapping operations depends on at least one indeterminate parameter, wherein the value to be determined of the at least one indeterminate parameter is estimated from the measurements of the different information sources (I 1, I 2, . . . , I m) and wherein the filter vector is not needed for estimating the at least one indeterminate parameter.

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: A method for classification of ground conditions in the vicinity of a vehicle using a radar sensor, comprising: receiving reflected portions of a radar signal at a receiver unit of a radar system; calculating information derived from the received portions of the radar signal for discrete spatial regions by the radar system or a control unit connected thereto; assigning the information to data structure units associated with a geographical location and the assignment of the information taking into account movement of the vehicle; collecting pieces of information in the respective data structure units, the pieces of information being obtained from reflected portions of radar signals transmitted at different times; evaluating the information contained in the data structure using a classifier to obtain information regarding the ground condition; assigning ground condition types to the data structure units based on evaluation results obtained by the classifier.

Abstract: An object tracking method, in which a radar sensor emits radar signals in successive measurement cycles, said radar signals being reflected by the object and captured by the radar sensor as radar targets, wherein movement information about the object for object tracking is determined on the basis of the radar targets and a search window for the radar targets of the object is defined on the basis of the movement information, wherein the search window is widened if a change in the movement information which exceeds a definable limit value is determined in successive measurement cycles and/or if no radar targets of the tracked object are captured anymore.

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 pressure compensation element includes a plastic part and a membrane, wherein the plastic part has an upper and an underside and at least one hole extending from the upper to the underside.

Abstract: The disclosure relates to a method and a system for the detection, 3D reconstruction and tracking of multiple rigid objects moving relative to one another from a series of images from at least one camera and can be used, in particular, in the context of a camera-based environment detection system for assisted or automated driving.

Abstract: A method for creating a road model for planning a trajectory of an ego vehicle, includes capturing a vehicle environment with at least one environment capturing sensor. Objects are detected in the vehicle environment. The method includes producing a grid map with a plurality of grid cells and entering the detections into the grid map wherein the detections are enlarged prior to being entered. The method also includes determining at least one path through the grid map which consists only of vacant grid cells. The at least one path is gradually scanned, wherein detections are determined orthogonally to a scanning direction of the path on both sides. The method further includes assigning the detections along the at least one path to road boundaries and creating the road model based on the previously determined information.

Abstract: The invention relates to a method for operating ultrasonic sensors) for a motor vehicle, including emitting a plurality of ultrasonic signals by respective ultrasonic sensors. The ultrasonic signals include a sequence of elementary signals. The elementary signals have signal pauses and a plurality of different signal pulses. The ultrasonic signals differ from one another by the sequence of the elementary signals. The method further includes receiving reflected ultrasonic signals, wherein the received ultrasonic signals are associated with the emitted ultrasonic signals on the basis of the sequences of the elementary signals.

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 lane and roadway detection uses a multitask DNN architecture including an encoder, a first decoder and a second decoder. The method includes the following steps: providing an input image by an optical detection device, filtering the input image by the encoder, generating a first representation of the lane and/or roadway by the encoder, processing the first representation in the first and second decoders, outputting two different representations from the first and second decoders, combining the two different representations, and outputting identified lanes, lane markings and the roadway.

Abstract: A method for processing image information of an imaging sensor of a vehicle in an artificial neural network (“ANN”) is disclosed. The ANN includes at least one encoder and one decoder. The ANN solves a classification task with a plurality of classes and/or a regression task, in which numerical output information quantized according to a plurality of quantization steps is provided. The ANN outputs multiple feature maps at the output interface, wherein allocations of image regions of the image information to classes or numerical output information quantized regarding the image information is/are output by the feature maps in an encoded manner.

Abstract: A method includes the steps: a) capturing image of an environment surrounding a vehicle by a camera; b) determining an area of increased brightness in the image based on pixels having a brightness exceeding a predefined threshold; c) estimating position coordinates of a light source in the environment from the area of increased brightness; d) detecting a shadow of the vehicle in the image; and e) determining a type of the light source as a spot light source or a direction light source from the shadow, environment.

Abstract: A surround view system for a vehicle includes a detection unit and an evaluation unit. The detection unit is designed to detect data relating to the surroundings. The evaluation unit is designed to identify an object in the detected surroundings data and to determine the 3D shape of this object. The evaluation unit is additionally designed to add the determined 3D shape to a projection surface of the surround view system to produce a modified projection surface. The evaluation unit is designed to project the surroundings data onto the modified projection surface.

Abstract: A method for detecting the environment of a motor vehicle utilizing a radar sensor includes bringing about frequency modulation with a controllable oscillator and generating a sequence of transmission-frequency-modulated transmit signals, which each have the same nominal frequency profile. Received signals reflected from objects are evaluated such that an actual profile of the transmission frequency within the transmit signals or a deviation of the actual profile from the nominal frequency profile is established. Depending on an actual profile and/or a deviation determined, correction in the driving of the oscillator and/or correction in the evaluation of the received signals and/or adaptation of the driver assistance function and/or the autonomous driving maneuver function up to and including disabling thereof are performed.

Abstract: A method for detecting the environment of a motor vehicle utilizing a radar system includes bringing about frequency modulation utilizing an oscillator and generating a sequence of transmission-frequency-modulated transmit signals, which each having the same nominal frequency profile, apart from a variation in frequency position. Received signals for object detection are evaluated. A one time-discrete signal per transmit signal is used which includes information about the frequency profile of the transmit signal and which is generated by sampling of an analog signal or by reading out of a free-running counter at predetermined points in time. These time-discrete signals are unnormalized by way of the transmit signals with regard to the position of their phase and/or their initial value.

Abstract: A method and apparatus for generating an image of vehicle surroundings are disclosed, including: capturing vehicle surroundings by vehicle cameras arranged on a vehicle body of a vehicle, and generating camera images by the cameras. The camera images of adjacent cameras have overlapping image regions), generating a virtual representation of the surroundings in a virtual three-dimensional space, during which the camera images are projected onto a virtual projection surface in the space. A non-stationary virtual camera in the virtual space determines a position and/or orientation thereof. A first selection region is placed on the surface in a first overlapping image region depending on a virtual camera field of vision, at least one image parameter of a first vehicle camera in the first selection region is calculated, and at least one second vehicle image parameter is adjusted to the at least one first vehicle image parameter in the first selection region.

Abstract: A method is disclosed for generating an image of vehicle surroundings, including: providing multiple vehicle cameras which are arranged in particular on a vehicle bodywork of a vehicle (S1). Individual HDR images are calculated and/or generated from image data or images from the vehicle cameras (S2). The multiple individual HDR images are assembled to produce an overall HDR image. An image is calculated having a low dynamic range, in particular an LDR image, from the overall HDR image. An apparatus is also disclosed for generating an image of vehicle surroundings.