Abstract: A computer implemented method for determining an angle of a detection comprises the following steps carried out by computer hardware components: acquiring a range rate of the detection; determining a pair of candidate angles of the detection based on the range rate; acquiring a beamvector of the detection; determining a correlation between the beamvector and a reference vector; and determining the angle of the detection based on the pair of candidate angles and based on the correlation.

Abstract: A method for increasing the reliability of determining the position of a vehicle on the basis of a plurality of detection points is described, the plurality of detection points being acquired using a radio-based and/or an optical sensor system, in particular a radar system, of a vehicle receiving electromagnetic signals from a vicinity of the vehicle, and each of the plurality of detection point representing a location in the vicinity of the vehicle, wherein the method comprises for each detection point of at least a subset of the plurality of detection points: determining at least one geometrical object associated with the detection point; determining at least one group of detection points from the subset sharing the at least one geometrical object; determining a quantity of detection points in the at least one group; evaluating a weight which represents the quantity of detection points in the at least one group on a predefined scale, and processing the detection point with respect to the weight.

Abstract: A method of determining an alignment error of an antenna is described, wherein the antenna is installed at a vehicle and in cooperation with a detection device, and wherein the detection device is configured to determine a plurality of detections. Determining the plurality of detections comprises emitting a first portion of electromagnetic radiation through the antenna, receiving a second portion of electromagnetic radiation through the antenna, and evaluating the second portion of electromagnetic radiation in dependence of the first portion of electromagnetic radiation in order to localize areas of reflection of the first portion of electromagnetic radiation in the vicinity of the antenna. The method comprises determining a first detection and at least a second detection by using the detection device, and determining the alignment error by means of a joint evaluation of the first detection and the second detection.

Abstract: A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.

Abstract: A method is described for determining the position of a vehicle equipped with a radar system that includes at least one radar sensor adapted to receive radar signals emitted from at least one radar emitter of the radar system and reflected the radar sensor. The method comprises: acquiring at least one radar scan comprising a plurality of radar detection points, wherein each radar detection point is evaluated from a radar signal received at the radar sensor and representing a location in the vicinity of the vehicle; determining, from a database, a predefined map, wherein the map comprises at least one element representing a static landmark in the vicinity of the vehicle; matching at least a subset of the plurality of radar detection points of the at least one scan and the at least one element of the map; deter-mining the position of the vehicle based on the matching.

Abstract: A computer-implemented method for determining an initial ego-pose for initialization of self-localization includes providing a plurality of particles in a map; grouping the particles in a plurality of clusters, each cluster comprising a respective subset of the plurality of particles; during particle filtering, injecting particles based on the plurality of clusters; and determining an initial ego-pose based on the particle filtering.

Abstract: A computer implemented method for determining an initial ego-pose for initialization of self-localization comprises the following steps carried out by computer hardware components: providing a plurality of particles in a map; grouping the particles in a plurality of clusters; performing particle filtering individually for each of the clusters; and determining an initial ego-pose based on the particle filtering.

Abstract: A computer implemented method for determining the position of a vehicle, wherein the method comprises: determining at least one scan comprising a plurality of detection points, wherein each detection point is evaluated from a signal received at the at least one sensor and representing a location in the vehicle environment; determining, from a database, a predefined map, wherein the map comprises a plurality of elements in a map environment, each of the elements representing a respective one of a plurality of static landmarks in the vehicle environment, and the map environment representing the vehicle environment; matching the plurality of detection points and the plurality of elements of the map; determining the position of the vehicle based on the matching; wherein the predefined map further comprises a spatial assignment of a plurality of parts of the map environment to the plurality of elements, and wherein the spatial assignment is used for the matching.

Abstract: A computer implemented method for detecting objects includes providing signal representation data comprising range information, velocity information and angular information; for each of a plurality of spatial scales, determining respective scaled data for the respective spatial scale based on the signal representation data, to obtain a plurality of scaled data; providing the plurality of scaled data to a plurality of detectors; and each detector carrying out object detection based on at least one of the plurality of scaled data.

Abstract: A method for fusing a first occupancy map and a second occupancy map comprises: determining at least one fusion parameter representing a potential dissimilarity between the first occupancy map and the second occupancy map and determining a fused occupancy map representing free and occupied space around the vehicle. The fused occupancy map is determined based on the first occupancy map, the second occupancy map, and a fusion rule. The fusion rule is configured to control the influence of the first occupancy map and/or the second occupancy map on the fused occupancy map based on the at least one fusion parameter.

Abstract: A method is described for determining the position of a vehicle equipped with a radar system that includes at least one radar sensor adapted to receive radar signals emitted from at least one radar emitter of the radar system and reflected the radar sensor. The method comprises: acquiring at least one radar scan comprising a plurality of radar detection points, wherein each radar detection point is evaluated from a radar signal received at the radar sensor and representing a location in the vicinity of the vehicle; determining, from a database, a predefined map, wherein the map comprises at least one element representing a static landmark in the vicinity of the vehicle; matching at least a subset of the plurality of radar detection points of the at least one scan and the at least one element of the map; deter-mining the position of the vehicle based on the matching.

Abstract: A method of determining an alignment error of an antenna is described, wherein the antenna is installed at a vehicle and in cooperation with a detection device, and wherein the detection device is configured to determine a plurality of detections. Determining the plurality of detections comprises emitting a first portion of electromagnetic radiation through the antenna, receiving a second portion of electromagnetic radiation through the antenna, and evaluating the second portion of electromagnetic radiation in dependence of the first portion of electromagnetic radiation in order to localize areas of reflection of the first portion of electromagnetic radiation in the vicinity of the antenna. The method comprises determining a first detection and at least a second detection by using the detection device, and determining the alignment error by means of a joint evaluation of the first detection and the second detection.

Abstract: A method of generating a confidence measure for an estimation derived from images captured by a camera mounted on a vehicle includes: capturing consecutive training images by the camera while the vehicle is moving; determining ground-truth data for the training images; computing optical flow vectors from the training images and estimating a first output signal based on the optical flow vectors for each of the training images, the first output signal indicating an orientation of the camera; classifying the first output signal for each of the training images as a correct signal or a false signal depending on how good the first output signal fits to the ground-truth data; determining optical flow field properties for each of the training images derived from the training images; and generating a separation function that separates the optical flow field properties into two classes based on the classification of the first output signal.

Abstract: A method for increasing the reliability of determining the position of a vehicle on the basis of a plurality of detection points is described, the plurality of detection points being acquired using a radio-based and/or an optical sensor system, in particular a radar system, of a vehicle receiving electromagnetic signals from a vicinity of the vehicle, and each of the plurality of detection point representing a location in the vicinity of the vehicle, wherein the method comprises for each detection point of at least a subset of the plurality of detection points: determining at least one geometrical object associated with the detection point; determining at least one group of detection points from the subset sharing the at least one geometrical object; determining a quantity of detection points in the at least one group; evaluating a weight which represents the quantity of detection points in the at least one group on a predefined scale, and processing the detection point with respect to the weight.

Abstract: A method of generating an occupancy map representing free and occupied space around a vehicle, wherein the occupancy map is divided into a plurality of cells Mx,y, the method includes: capturing two consecutive images by a camera mounted on the vehicle; generating optical flow vectors from the two consecutive images; estimating 3D points in the space around the vehicle from the optical flow vectors; generating rays from the camera to each of the estimated 3D points, wherein intersection points of the rays with the cells Mx,y defining further 3D points; determining for each of the cells Mx,y a function L_(x,y)^t for a time step t; and determining an occupancy probability from the function L_(x,y)^t for each of the cells Mx,y.

Abstract: A method of generating an occupancy map representing free and occupied space around a vehicle, wherein the occupancy map is divided into a plurality of cells Mx,y, the method includes: capturing two consecutive images by a camera mounted on the vehicle; generating optical flow vectors from the two consecutive images; estimating 3D points in the space around the vehicle from the optical flow vectors; generating rays from the camera to each of the estimated 3D points, wherein intersection points of the rays with the cells Mx,y defining further 3D points; determining for each of the cells Mx,y a function L_(x,y)?t for a time step t; and determining an occupancy probability from the function L_(x,y)?t for each of the cells Mx,y.

Abstract: A method of estimating an orientation of a camera relative to a surface includes providing a first image and a subsequent second image captured by the camera; selecting a first point from the first image and a second point from the second image, where the first and second points represent the same object; defining a first optical flow vector connecting the first point and the second point; carrying out a first estimation step comprising estimating two components of the normal vector in the camera coordinate system by using the first optical flow vector and restricting parameter space to only the two components of the normal vector, wherein a linear equation system derived from a homography matrix that represents a projective transformation between the first image and the second image is provided and the two components of the normal vector in the camera coordinate system are estimated by solving the linear equation system; and determining the orientation of the camera relative to the surface using the results

Abstract: A method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device. The object is recognized with an object-class by means of an image processing system. Respective position data are determined from the images using a pinhole camera model based on the object-class. Position data indicating in world coordinates the position of a reference point of the object with respect to the plane of the road is used with a scaling factor of the pinhole camera model estimated by means of a Bayes estimator using the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability. The distance of the object from the automated vehicle is calculated from the estimated scaling factor using the pinhole camera model.

Abstract: A method of generating a confidence measure for an estimation derived from images captured by a camera mounted on a vehicle includes: capturing consecutive training images by the camera while the vehicle is moving; determining ground-truth data for the training images; computing optical flow vectors from the training images and estimating a first output signal based on the optical flow vectors for each of the training images, the first output signal indicating an orientation of the camera; classifying the first output signal for each of the training images as a correct signal or a false signal depending on how good the first output signal fits to the ground-truth data; determining optical flow field properties for each of the training images derived from the training images; and generating a separation function that separates the optical flow field properties into two classes based on the classification of the first output signal.

Abstract: A method of estimating an orientation of a camera relative to a surface includes providing a first image and a subsequent second image captured by the camera; selecting a first point from the first image and a second point from the second image, where the first and second points represent the same object; defining a first optical flow vector connecting the first point and the second point; carrying out a first estimation step comprising estimating two components of the normal vector in the camera coordinate system by using the first optical flow vector and restricting parameter space to only the two components of the normal vector, wherein a linear equation system derived from a homography matrix that represents a projective transformation between the first image and the second image is provided and the two components of the normal vector in the camera coordinate system are estimated by solving the linear equation system; and determining the orientation of the camera relative to the surface using the results