Aptiv Technologies Limited

Delphi Technologies, Inc.

Aptiv Technologies Ag

Christian Nunn

Mirko Meuter

Alexander Barth

Yu Su

Weimeng Zhu

Dennis Mueller

Kun Zhao

Jan Siegemund

Adrian Becker

Florian Kaestner

Andre Paus

Markus Schoeler

Stefan Mueller-Schneiders

Mirko Mueter

Lech J Szumilas

Anslem Haselhoff

Steffen Goermer

Dennis Müller

Lech Szumilas

A computer implemented method for determining a location of an object comprises the following steps carried out by computer hardware components: determining a pre-stored map of a vicinity of the object; acquiring sensor data related to the vicinity of the object; determining an actual map based on the acquired sensor data; carrying out image registration based on the pre-stored map and the actual map; carrying out image registration based on the image retrieval; and determining a location of the object based on the image registration.

Methods and system for determining a location of an object

A device for processing data sequences by means of a convolutional neural network is configured to carry out the following steps: receiving an input sequence comprising a plurality of data items captured over time using a sensor, each of said data items comprising a multi-dimensional representation of a scene, generating an output sequence representing the input sequence processed item-wise by the convolutional neural network, wherein generating the output sequence comprises: generating a grid-generation sequence based on a combination of the input sequence and an intermediate grid-generation sequence representing a past portion of the output sequence or the grid-generation sequence, generating a sampling grid on the basis of the grid-generation sequence, generating an intermediate output sequence by sampling from the past portion of the output sequence according to the sampling grid, and generating the output sequence based on a weighted combination of the intermediate output sequence and the input sequence.

Device and a method for processing data sequences using a convolutional neural network

A device for extracting dynamic information comprises a convolutional neural network, wherein the device is configured to receive a sequence of data blocks acquired over time, each of said data blocks comprising a multi-dimensional representation of a scene. The convolutional neural network is configured to receive the sequence as input and to output dynamic information on the scene in response, wherein the convolutional neural network comprises a plurality of modules, and wherein each of said modules is configured to carry out a specific processing task for extracting the dynamic information.

Device and a method for extracting dynamic information on a scene using a convolutional neural network

A method for validation of an obstacle candidate identified within a sequence of image frames comprises the following steps: A. for a current image frame of the sequence of image frames, determining within the current image frame a region of interest representing the obstacle candidate, dividing the region of interest into sub-regions, and, for each sub-region, determining a Time-To-Contact (TTC) based on at least the current image frame and a preceding or succeeding image frame of the sequence of image frames; B. determining one or more classification features based on the TTCs of the sub-regions determined for the current image frame; and C. classifying the obstacle candidate based on the determined one or more classification features.

Method for validation of obstacle candidate

An image processing method includes: determining a candidate track in an image of a road, wherein the candidate track is modelled as a parameterized line or curve corresponding to a candidate lane marking in the image of a road; dividing the candidate track into a plurality of cells, each cell corresponding to a segment of the candidate track; determining at least one marklet for a plurality of said cells, wherein each marklet of a cell corresponds to a line or curve connecting left and right edges of the candidate lane marking; determining at least one local feature of each of said plurality of cells based on characteristics of said marklets; determining at least one global feature of the candidate track by aggregating the local features of the plurality of cells; and determining if the candidate lane marking represents a lane marking based on the at least one global feature.

Image based lane marking classification

A method for lane detection for a camera-based driver assistance system includes the following steps: image regions in images that are recorded by a camera are identified as detected lane markings if the image regions meet a specified detection criterion. At least two detected lane markings are subjected to a tracking process as lane markings to be tracked. By means of a recursive state estimator, separate progressions are estimated for at least two of the lane markings to be tracked. Furthermore, for each of a plurality of the detected lane markings, a particular offset value is determined, which indicates a transverse offset of the detected lane marking in relation to a reference axis. By means of an additional estimation method, the determined offset values are each associated with one of the separate progressions of the lane markings to be tracked.

Method for lane detection

In a method of generating a training image for teaching of a camera-based object recognition system suitable for use on an automated vehicle which shows an object to be recognized in a natural object environment, the training image is generated as a synthetic image by a combination of a base image taken by a camera and of a template image in that a structural feature is obtained from the base image and is replaced with a structural feature obtained from the template image by means of a shift-map algorithm.

Method of generating a training image for an automated vehicle object recognition system

In a method for the detection and tracking of lane markings from a motor vehicle, an image of a space located in front of the vehicle is captured by means of an image capture device at regular intervals. The picture elements that meet a predetermined detection criterion are identified as detected lane markings in the captured image. At least one detected lane marking as a lane marking to be tracked is subjected to a tracking process. At least one test zone is defined for each detected lane marking. With the aid of intensity values of the picture elements associated with the test zone, at least one parameter is determined. The detected lane marking is assigned to one of several lane marking categories, depending on the parameter.

Method for the detection and tracking of lane markings

The invention relates to a method for the prediction of the size to be expected of the image of a stationary object associated with a road in a picture of the environment in the field of view of a camera device which is in particular arranged at a motor vehicle and which has an image plane including image elements, wherein at least one relevant spatial zone from the field of view of the camera device is determined; wherein boundaries of the calculated projection onto the image plane of the at least one relevant spatial zone are determined in order to determine at least one relevant image zone; wherein a directional beam is determined for each of the image elements in the at least one relevant image zone, said directional beam including those spatial points from the field of view which would be projected onto the respective image element on a projection onto the image plane; and wherein at least one value for the size to be expected of the image of a road sign in the respective image element is determined for each of the image elements in the relevant image zone.

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Huckeswagen, Germany

Christian Nunn