Delphi Technologies, Inc.

Aptiv Technologies Ag

Aptiv Technologies Limited

Dennis Mueller

Mirko Meuter

Stephanie Lessmann

Alexander Barth

Christian Nunn

Yu Su

Lutz Roese-Koerner

Daniel Schugk

Farzin G Rajabizadeh

Narges Milani

Kun Zhao

Peet Cremer

Michael Arnold

Sönke Behrends

Mirko Mueter

Lech J Szumilas

Anslem Haselhoff

Steffen Goermer

Disclosed are aspects of a method, carried out by a vehicle data recording device, that includes downloading, from a host data collecting system, a recording target and determining a plurality of possible routes for the vehicle. The method also includes, for each route, generating a route encoding in numerical values an information on predicted values of the route for metrics, a metric being a function assigning a value representing an amount of progress in achieving an elementary recording target to a piece of data. The method additionally includes providing the route encodings and additional environmental information independent of the routes to a reinforcement learning agent that selects one of the routes to optimize a reward. The method further includes recording data from in-vehicle sources while the vehicle drives along the selected route and uploading at least part of the recording data to the host data collecting system.

Method of selecting a route for recording vehicle

A method of processing image data in a connectionist network comprises a plurality of units, wherein the method implements a multi-channel unit forming a respective one of the plurality of units, and wherein the method comprises: receiving, at the data input, a plurality of input picture elements representing an image acquired by means of a multi-channel image sensor, wherein the plurality of input picture elements comprise a first and at least a second portion of input picture elements, wherein the first portion of input picture elements represents a first channel of the image sensor and the second portion of input picture elements represents a second channel of the image sensor; processing of the first and at least second portion of input picture elements separately from each other; and outputting, at the data output, the processed first and second portions of input picture elements.

Methods of processing and generating image data in a connectionist network

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

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.

Method to determine distance of an object from an automated vehicle with a monocular device

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

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Moers, Germany

Dennis Mueller