Aptiv Technologies Limited

Aptiv Technologies Ag

Delphi Technologies, Inc.

Stephanie Lessmann

Mirko Meuter

Uri Iurgel

Ahmad Pishehvari

Jan Siegemund

Wolfgang Doerr

Jens Westerhoff

Weimeng Zhu

Igor Kossaczký

Saeid Khalili Dehkordi

Sven Labusch

Alexander Ioffe

Yu Su

Dennis Mueller

Honghui Yan

Adrian Becker

Lutz Roese-Koerner

Markus Schoeler

Arne Grumpe

Peet Cremer

Florian Kästner

Dominic Spata

Moritz Luszek

Alessandro Cennamo

Markus Stefer

Simon Roesler

Jittu Kurian

Zhiheng Niu

Ori Maoz

Marco Braun

Kevin Kollek

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.

Method and system for object detection

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.

Methods and systems for detection of objects in a vicinity of a vehicle

A method is provided for estimating an angle of an object with respect to a vehicle. A transformation of reflected radar signals is calculated, wherein the result of the transformation depends on a range with respect to the vehicle. A long beam vector is generated for respective range bins provided by the transformation by rearranging the result of the transformation such that the respective long beam vector comprises elements of the transformation from radar all receiver elements for each range bin. A reference vector is calculated for each range bin based on a signal model which depends on the motion of the target object relative to the vehicle and which is parameterized regarding the angle of the target object. The long beam vector and the reference vector are correlated, and the angle of the target object is determined based on the correlation result.

Method and device for determining an angle of a target object

Described is a way to process radar data for a radar sensor mounted on a vehicle to generate motion spectrum data for estimating ego-motion information of the vehicle. Data samples of each of a plurality of radar return signals received at each antenna element of the radar sensor are generated for each antenna element. Respective Doppler-processed data including a plurality of data values is calculated for each Doppler bin index. In generating a set of motion spectrum data, which comprises a plurality of data elements each calculated for a respective Doppler bin index and a respective spatial bin index, data values of the Doppler-processed data calculated for the Doppler bin index are selected to calculate a covariance matrix. A spectral estimation algorithm, which uses the covariance matrix, can determine a spatial spectrum value for each spatial bin index.

Radar data processing for vehicle ego-motion estimation

A method of determining ego-motion information of a vehicle comprising a radar sensor having a plurality of antenna elements, comprising: acquiring motion spectrum comprising a plurality of data elements, each calculated for a respective one of a plurality of Doppler bin indices and for a respective one of a plurality of spatial bin indices, each spatial bin index indicating a respective angle-of-arrival of a radar return signal at the radar sensor; and determining the ego-motion information by solving a motion equation system comprising equations of motion generated using the motion spectrum data and each relating a respective value indicating a radial velocity, a respective value indicating an angular displacement, and a variable indicating a velocity of the vehicle.

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.

Method for determining the position of a vehicle

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.

Methods and system for determining an angle of a detection

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.

Methods and systems for determining an initial ego-pose for initialization of self-localization

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.

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.

Method for increasing the reliability of determining the position of a vehicle on the basis of a plurality of detection points

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.

Method of determining an alignment error of an antenna and vehicle with an antenna and a detection device

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.

Method and a device for generating a confidence measure for an estimation derived from images captured by a camera mounted on a vehicle

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.

Method and a device for generating an occupancy map of an environment of a vehicle

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 of the first estimation step.

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Erkrath, Germany

Stephanie Lessmann