Aptiv Technologies Limited

Aptiv Technologies Ag

Yu Su

Weimeng Zhu

Christian Nunn

Mirko Meuter

Kun Zhao

Dennis Mueller

Adrian Becker

Lutz Roese-Koerner

Markus Schoeler

Daniel Schugk

Florian Kästner

Farzin G Rajabizadeh

Narges Milani

Stephanie Lessmann

Jan Siegemund

Arne Grumpe

Andre Paus

Alessandro Cennamo

Dominic Spata

Moritz Luszek

Simon Roesler

Zhiheng Niu

Jittu Kurian

Igor Kossaczký

Saeid Khalili Dehkordi

Sven Labusch

Marco Braun

Kevin Kollek

Farzin Ghorban Rajabizadeh

Dennis Müller

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 assigning a bounding box to an object in an environment of a vehicle. Data related to objects located in the environment of the vehicle are acquired via a sensor. Based on the data, a respective spatial location and a respective size of a plurality of preliminary bounding boxes are determined such that each preliminary bounding box covers one of the objects at least partly. A respective velocity of each preliminary bounding box is estimated based on the data. A subset of the plurality of preliminary bounding boxes being related to a respective one of the objects is selected, where the subset is selected based on the respective velocity of each of the preliminary bounding boxes. A final bounding box is assigned to the respective one of the objects by merging the preliminary bounding boxes of the corresponding subset.

Method and a device for assigning a bounding box to an object

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 computer implemented method for object detection the following steps carried out by computer hardware components: determining an output of a first pooling layer based on input data; determining an output of a dilated convolution layer, provided directly after the first pooling layer, based on the output of the first pooling layer; determining an output of a second pooling layer, provided directly after the dilated convolution layer, based on the output of the dilated convolution layer; and carrying out the object detection based on at least the output of the dilated convolution layer or the output of the second pooling layer.

Methods and systems for object detection

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 computer implemented method for object detection includes: determining a grid, the grid comprising a plurality of grid cells; determining, for a plurality of time steps, for each grid cell, a plurality of respective radar detection data, each radar detection data indicating a plurality of radar properties; determining, for each time step, a respective radar map indicating a pre-determined radar map property in each grid cell; converting the respective radar detection data of the plurality of grid cells for the plurality of time steps to a point representation of pre-determined first dimensions; converting the radar maps for the plurality of time steps to a map representation of pre-determined second dimensions, wherein the pre-determined first dimensions and the pre-determined second dimensions are at least partially identical; concatenating the point representation and the map representation to obtain concatenated data; and carrying out object detection based on the concatenated data.

A method of multi-sensor data fusion includes determining a plurality of first data sets using a plurality of sensors, each of the first data sets being associated with a respective one of a plurality of sensor coordinate systems, and each of the sensor coordinate systems being defined in dependence of a respective one of a plurality of mounting positions for the sensors; transforming the first data sets into a plurality of second data sets using a transformation rule, each of the second data sets being associated with a unified coordinate system, the unified coordinate system being defined in dependence of at least one predetermined reference point; and determining at least one fused data set by fusing the second data sets.

Method of multi-sensor data fusion

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 device for searching for a lane on which a vehicle can drive, wherein the device is configured to receive an image captured by a camera, the image showing an area in front of the vehicle, detect lane markings in the image, determine for each of the lane markings if the respective lane marking is of a first type indicating a road condition of a first type or a second type indicating a road condition of a second type, create lane candidates from the lane markings, divide the lane candidates into classes of lane candidates depending on the type of the lane markings of the lane candidates, and search the classes of lane candidates for a lane on which the vehicle can drive.

Device and a method for searching for a lane on which a vehicle can drive

The present invention relates to a method and an image processing system for determining the color of a street marking, by: capturing an image of a street, detecting a street marking as a set of pixels provided by the image, wherein the pixels include at least two different pieces of color information, determining a color score for the street marking by comparing said at least two different pieces of color information, and determining the color of the street marking by comparing the color score to at least one threshold value.

Street marking color recognition

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.

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Wuppertal, Germany

Yu Su